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-license: cc-by-nc-sa-4.0
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: meta_data.jsonl
+license:
+  - cc-by-nc-sa-4.0
+  - cc-by-4.0
+  - cc-by-nc-3.0
+  - cc-by-nc-4.0
+  - odc-by
+  - mit
+task_categories:
+  - audio-classification
+  - question-answering
+language:
+  - en
+tags:
+  - machine hearing
+  - audio
+  - time series
+  - timeseries
+  - benchmark
+  - classification
+  - scientific signals
+  - multimodal llm
+size_categories:
+  - 1K<n<10K
+pretty_name: 'Machine Hearing Benchmark (MHB)'
 ---
+
+# Machine Hearing Benchmark (MHB)
+
+![Overview of MHB across seven domains and 13 tasks](process/MHB.png)
+
+This repository provides the **Machine Hearing Benchmark (MHB)** introduced in [**Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception?**](https://arxiv.org/abs/2611.10693)
+
+Multimodal LLMs have made substantial progress in perceiving the physical world, but their capabilities remain largely centered on signals that humans can directly see or hear. The physical world contains waves, vibrations, and oscillations far beyond human auditory perception. MHB investigates whether **Audio Large Language Models (ALLMs)** can generalize to this broader form of *machine hearing*.
+
+MHB comprises **6,128 test instances across 13 classification tasks and seven domains**: Astronomy, Earth Science, Healthcare, Electromagnetic, Underwater Acoustics, Bioacoustics, and Manufacturing. It spans highly heterogeneous input regimes, with sampling rates from approximately $10^{-5}$ to $10^6$ Hz, sequence lengths from $10^1$ to $10^6$ samples, and both single- and multi-channel inputs. Each instance consists of a signal, a task-specific textual instruction, and a predefined label set.
+
+## Dataset Licenses and Distribution
+
+MHB integrates data from multiple sources. The following table lists the source or processed-release licenses and whether the MHB input signals are included in this repository. **These licenses apply to their respective materials; the list of licenses in the Dataset Card metadata does not mean that each file is offered under every listed license.**
+
+| MHB Task | Data Source | Source / Release License | Input Files | Reference |
+| --- | --- | --- | --- | --- |
+| GWOSC | GWOSC_GW_Event via SciTS | CC BY-NC-SA 4.0 (SciTS) | Included | [SciTS](https://huggingface.co/datasets/OpenTSLab/SciTS) |
+| LEAVES | LEAVES | CC BY 4.0 (original data; note SciTS release terms) | Included | [NADC](https://nadc.china-vo.org/res/r100962/?lang=en) |
+| Iquique | Iquique via SeisBench | Not specified | **Reconstruction required** | [SeisBench](https://seisbench.readthedocs.io/en/main/pages/data/benchmark_datasets.html#iquique) |
+| Llaima | Llaima | CC BY-NC 3.0 | Included | [Mendeley Data](https://data.mendeley.com/datasets/dv8nwdd36k/1) |
+| Yaseen | Yaseen | Not specified | **Reconstruction required** | [Official GitHub](https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-) |
+| CirCor_loc | CirCor | ODC-BY 1.0 | Included | [PhysioNet](https://physionet.org/content/circor-heart-sound/1.0.3/) |
+| RadSeg | RadSeg | MIT | Included | [RadSeg](https://github.com/abcxyzi/radseg) |
+| RadioML | RadioML2016.10A | CC BY-NC-SA 4.0 | Included | [DeepSig](https://www.deepsig.ai/datasets/) |
+| ShipsEar | ShipsEar | Not specified | **Reconstruction required** | [Official database](https://underwaternoise.atlanttic.uvigo.es/) |
+| DeepShip | DeepShip | Not specified | **Reconstruction required** | [Official GitHub](https://github.com/irfankamboh/DeepShip) |
+| MarmAudio | MarmAudio | CC BY 4.0 | Included | [Zenodo](https://zenodo.org/records/15017207) |
+| SDUST_bear | `sdust_bearing` via RMIS | MIT (RMIS processed release) | Included | [RMIS](https://huggingface.co/datasets/jiangab/RMIS) |
+| PU_cur | `pu_cur` via RMIS | MIT (RMIS processed release) | Included | [RMIS](https://huggingface.co/datasets/jiangab/RMIS) |
+
+**License notes.** MHB's original benchmark contributions, to the extent the maintainers hold the relevant rights, are released under **CC BY-NC-SA 4.0**. Original data sources and processed releases retain their own applicable conditions and attribution requirements. No waveform files from Iquique, Yaseen, ShipsEar, or DeepShip are redistributed here; their metadata and reconstruction scripts are provided instead.
+
+## Dataset Structure
+
+The repository contains a unified `meta_data.jsonl` file, task-specific data folders, and processing scripts. Each task folder follows the naming convention `Domain-Task-Task_Name`, with input signals stored under `raw_input/`.
+
+```text
+├── README.md
+├── meta_data.jsonl
+├── process/
+│   ├── MHB.png
+│   ├── map_deepship.py
+│   ├── map_iquique.py
+│   ├── map_shipsear.py
+│   ├── map_yaseen.py
+│   └── process_raw2wav.py
+├── Astronomy-GWOSC-Gravitational_Wave_Event_Detection/
+│   ├── raw_input/
+│   └── wav_input/                  # generated on demand
+├── Astronomy-LEAVES-Light_Curve_Classification/
+│   └── raw_input/
+├── ...
+└── Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/
+    └── raw_input/
+
+# wav_data.jsonl is generated at the repository root.
+```
+
+- **`meta_data.jsonl`**: The complete set of 6,128 test instances and annotations, including entries for the four datasets that must be obtained externally.
+- **`<Dataset_Folder>/raw_input/`**: Signal files in their MHB original formats (`csv`, `npy`, `wav`, or `flac`). Directly hosted tasks contain their files; the four externally sourced tasks are reconstructed locally.
+- **`process/map_*.py`**: Scripts for selecting and mapping externally obtained data to the exact names and relative paths in `meta_data.jsonl`.
+- **`process/process_raw2wav.py`**: Optional conversion of six non-WAV tasks to `wav_input/`, also generating `wav_data.jsonl`.
+
+The four externally hosted datasets can be reconstructed locally using the corresponding mapping scripts. The `wav_input/` directories and `wav_data.jsonl` are generated on demand by `process/process_raw2wav.py`.
+
+## Tasks
+
+All 13 tasks in MHB are formulated as classification tasks, with detailed task definitions provided in [Appendix A.1 of the paper](https://arxiv.org/abs/2611.10693).
+
+| Domain | Task | Instances | Classification Objective | Dataset Folder |
+| --- | --- | ---: | --- | --- |
+| Astronomy | GWOSC | 512 | Gravitational-wave event detection | `Astronomy-GWOSC-Gravitational_Wave_Event_Detection` |
+| Astronomy | LEAVES | 1,024 | Variable-star classification from light curves | `Astronomy-LEAVES-Light_Curve_Classification` |
+| Earth Science | Iquique | 502 | Earthquake focal-depth classification | `Earth_Science-Iquique-Earthquake_Focal_Depth_Classification` |
+| Earth Science | Llaima | 358 | Volcanic seismic-event classification | `Earth_Science-Llaima-Volcanic_Seismic_Event_Classification` |
+| Healthcare | Yaseen | 200 | Heart-sound classification | `Healthcare-Yaseen-Heart_Sound_Classification` |
+| Healthcare | CirCor_loc | 697 | Auscultation-location identification from PCG recordings | `Healthcare-CirCor_loc-Auscultation_Location_Identification` |
+| Electromagnetic | RadSeg | 482 | Radar waveform/coding-scheme classification | `Electromagnetic-RadSeg-Coding_Scheme-Classification` |
+| Electromagnetic | RadioML | 550 | Radio modulation classification | `Electromagnetic-RadioML-Radio_Modulation_Classification` |
+| Underwater Acoustics | ShipsEar | 84 | Vessel and sound-source classification | `Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification` |
+| Underwater Acoustics | DeepShip | 527 | Vessel-type classification | `Underwater_Acoustics-DeepShip-Vessel_Type_Classification` |
+| Bioacoustics | MarmAudio | 300 | Marmoset vocalization-type classification | `Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification` |
+| Manufacturing | SDUST_bear | 572 | Bearing fault diagnosis from vibration signals | `Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis` |
+| Manufacturing | PU_cur | 320 | Bearing fault diagnosis from motor-current signals | `Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis` |
+| **Total** | **13 tasks** | **6,128** | | |
+
+## `meta_data.jsonl` Format
+
+Each line is a JSON object representing one test instance. The following is a shortened example; the text fields are illustrative:
+
+```json
+{
+  "task_name": "GWOSC",
+  "dataset_name": "GWOSC_GW_Event",
+  "id": "GW_3413075",
+  "data_type": "csv",
+  "input_ts": {
+    "num_channel": 1,
+    "channel_detail": [],
+    "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003747_gn_m1_14_87_m2_3_63_mtotal_18_q_4_1.csv",
+    "length": 64000,
+    "timestamps": [],
+    "fs": 16000
+  },
+  "input_text": "Task-specific classification instruction",
+  "gt_text": "The classification result is: normal",
+  "gt_result": {
+    "class_list": ["normal", "anomaly"],
+    "gt_class": ["normal"]
+  },
+  "meta_data": {}
+}
+```
+
+| Field | Description |
+| --- | --- |
+| `task_name` | MHB task identifier (e.g., `GWOSC`, `CirCor_loc`, `SDUST_bear`). |
+| `dataset_name` | Original or processed-source dataset identifier. |
+| `id` | Unique identifier of the instance. |
+| `data_type` | Input file format: `csv`, `npy`, `wav`, or `flac`. |
+| `input_ts.path` | Path to the input file, **relative to the MHB repository root**. For externally sourced datasets, the corresponding mapping script restores this file locally. |
+| `input_ts.length` | Number of time steps in the original signal; multi-channel signals have additional elements across channels. |
+| `input_ts.num_channel` | Number of signal channels. |
+| `input_ts.channel_detail` | Optional channel descriptions; empty when not provided. |
+| `input_ts.fs` | Original signal sampling frequency in Hz. |
+| `input_ts.timestamps` | Optional timestamp information, usually an empty list. |
+| `input_text` | Task-specific textual instruction given to the model. |
+| `gt_text` | Reference answer in text form. |
+| `gt_result.class_list` | List of valid classification labels. |
+| `gt_result.gt_class` | Ground-truth class label as a list. |
+| `meta_data` | Source-specific metadata (e.g., `trace_name` for Iquique or `ori_file_path` and `seg_index` for ShipsEar); fields vary by task. |
+
+Paths in `meta_data.jsonl` are not absolute paths. For example, load a signal by joining the repository root with `record["input_ts"]["path"]`.
+
+For models that generate additional explanations or text, users may append an output-format constraint to the end of `input_text` during inference, such as: "Output ONLY the classification result. Do not output any other text, explanation, or punctuation."
+
+## Reconstructing Externally Hosted Datasets
+
+The waveforms of **Iquique, Yaseen, ShipsEar, and DeepShip** are not bundled in this release. Download the source data from the linked official providers, then run the relevant script from the **MHB repository root**. Each script uses `meta_data.jsonl` to select the exact MHB test subset, creates the required task folder and `raw_input/` directory, and preserves the relative paths already defined in the metadata. No changes to `meta_data.jsonl` are needed.
+
+For the MHB mapping scripts, install the necessary Python packages:
+
+```bash
+pip install numpy h5py soundfile
+```
+
+The upstream split tools, when used, may have their own dependencies.
+
+### Iquique (502 instances)
+
+**Source:** [SeisBench — Iquique](https://seisbench.readthedocs.io/en/main/pages/data/benchmark_datasets.html#iquique).
+
+Obtain the SeisBench Iquique dataset and locate the folder containing **`waveforms.hdf5`** and **`metadata.csv`**. Run:
+
+```bash
+python process/map_iquique.py /path/to/Iquique
+```
+
+The script uses `meta_data.trace_name` to extract the specified three-component seismic traces from the HDF5 file and saves 502 NPY files with the names and paths defined in `meta_data.jsonl`.
+
+### Yaseen (200 instances)
+
+**Source:** [Official Yaseen GitHub repository](https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-).
+
+Download or clone the repository and extract the five category archives (`AS`, `MR`, `MS`, `MVP`, and `N`). The extracted directories are named `AS_New_3주기/`, `MR_New_3주기/`, `MS_New_3주기/`, `MVP_New_3주기/`, and `N_New_3주기/`. For example, from the downloaded source directory:
+
+```bash
+for archive in *_New_3주기.rar; do unrar x "$archive"; done
+```
+
+Use **`unrar` or a recent compatible RAR extractor**. Then, from the MHB root:
+
+```bash
+python process/map_yaseen.py /path/to/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-
+```
+
+The script selects and copies the 200 specified WAV files from the five categories.
+
+### ShipsEar (84 instances)
+
+**Original data:** [ShipsEar official website](https://underwaternoise.atlanttic.uvigo.es/) (access to the complete dataset may require contacting its authors).
+
+**Train/test split:** [ShipsEar — An Unofficial Train-Test Split](https://github.com/YuanX9/ShipsEar-An-Unofficial-Train-Test-Split), using its **12-class split**.
+
+Download ShipsEar and apply the linked repository's 12-class `train-test-split.py` to produce a folder such as:
+
+```text
+shipsear_with_split-12class/
+├── train/
+└── test/
+```
+
+Then run:
+
+```bash
+python process/map_shipsear.py /path/to/shipsear_with_split-12class/test
+```
+
+Following the segmentation protocol associated with the referenced **12-class split**, the mapping script extracts **30-second windows at 15-second intervals**, including the designated Trawler test excerpt, and writes the 84 MHB test WAV files to their specified `raw_input/` paths.
+
+### DeepShip (527 instances)
+
+**Original data:** [Official DeepShip GitHub](https://github.com/irfankamboh/DeepShip).
+
+**Train/test split:** [Method for Splitting the DeepShip Dataset](https://github.com/Noxo02/Method-for-Splitting-the-DeepShip-Dataset).
+
+Download DeepShip and apply the linked repository's `train.txt`, `test.txt`, and `code.ipynb` to obtain the segmented test data. The split code generates a directory named **`DeepShip_test`**, with the following structure:
+
+```text
+DeepShip_test/
+├── Cargo/
+├── Passengership/
+├── Tanker/
+│   └── 20170822-133/
+│       └── 172736.wav/
+│           └── 00027.wav
+└── Tug/
+```
+
+Pass the path to the **actual generated `DeepShip_test` directory** (the one containing the four category folders):
+
+```bash
+python process/map_deepship.py /path/to/DeepShip_test
+```
+
+The script selects the 527 designated three-second test segments, renames each output WAV using the sample `id`, and copies the files to the corresponding `raw_input/` location.
+
+## RAW-to-WAV Conversion
+
+For Audio LLM APIs that only accept WAV files, MHB provides `process/process_raw2wav.py`. **First reconstruct Iquique**, because its NPY inputs are not included in the repository. From the MHB root, run:
+
+```bash
+pip install numpy pandas
+python process/process_raw2wav.py
+```
+
+The script converts **GWOSC, LEAVES, Iquique, Llaima, RadSeg, and RadioML** (3,428 instances) into each task's `wav_input/` directory.
+
+The script also generates `wav_data.jsonl` in the MHB root, containing **all 6,128 instances**. For the six converted tasks, it updates `data_type` and `input_ts.path`, `length`, `num_channel`, and `fs`; it additionally sets `input_ts.channel_detail` to `[]` for **Iquique, RadSeg, and RadioML**. The other seven tasks remain unchanged and retain their original WAV/FLAC input references where applicable.
+
+## Citation
+
+If you use MHB, please cite:
+
+```bibtex
+@misc{liu2026machinehearing,
+  title         = {Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception?},
+  author        = {Liwei Liu and Jun Tao and Xingyuan Li and Xuenan Xu and Chen Zhang and Junyi Wang and Boya Zhang and Xiaoyu Yang and Zhen-Hua Ling and Wen Wu and Chao Zhang},
+  year          = {2026},
+  eprint        = {2611.10693},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.SD},
+  url           = {https://arxiv.org/abs/2611.10693}
+}
+```
+
+Please also cite the original dataset publications and the relevant processing or split releases, including [SciTS](https://huggingface.co/datasets/OpenTSLab/SciTS), [RMIS/FISHER](https://huggingface.co/datasets/jiangab/RMIS), and the referenced [ShipsEar](https://github.com/YuanX9/ShipsEar-An-Unofficial-Train-Test-Split) / [DeepShip](https://github.com/Noxo02/Method-for-Splitting-the-DeepShip-Dataset) split methods, as applicable.
diff --git a/meta_data.jsonl b/meta_data.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9a809b8754fa934b15769973f04762eac1109183
--- /dev/null
+++ b/meta_data.jsonl
@@ -0,0 +1,6128 @@
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413075", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003747_gn_m1_14_87_m2_3_63_mtotal_18_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413064", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000627_gn_m1_93_45_m2_10_05_mtotal_103_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413112", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003648_cbc_m1_88_55_m2_9_95_mtotal_98_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413171", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007669_gn_m1_56_24_m2_43_26_mtotal_99_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002936_cbc_m1_69_69_m2_25_81_mtotal_95_q_2_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413083", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004801_gn_m1_34_8_m2_8_7_mtotal_43_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413200", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007275_gn_m1_35_3_m2_4_2_mtotal_39_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413198", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006939_gn_m1_26_04_m2_14_46_mtotal_40_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413188", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006120_cbc_m1_109_26_m2_35_24_mtotal_144_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413268", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008778_cbc_m1_6_87_m2_1_63_mtotal_8_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413280", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004613_gn_m1_63_0_m2_52_5_mtotal_115_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413277", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008401_gn_m1_130_74_m2_13_76_mtotal_144_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413267", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009674_cbc_m1_71_0_m2_35_5_mtotal_106_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413309", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009256_cbc_m1_112_12_m2_18_38_mtotal_130_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413346", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007773_gn_m1_54_0_m2_22_5_mtotal_76_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413317", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002411_gn_m1_86_35_m2_14_15_mtotal_100_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005990_cbc_m1_31_91_m2_3_59_mtotal_35_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413394", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006086_cbc_m1_36_56_m2_4_94_mtotal_41_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413434", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007402_cbc_m1_62_33_m2_9_17_mtotal_71_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413498", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007321_gn_m1_98_95_m2_14_55_mtotal_113_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008607_gn_m1_114_48_m2_22_02_mtotal_136_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006304_cbc_m1_50_69_m2_29_81_mtotal_80_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413519", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008991_gn_m1_124_24_m2_17_26_mtotal_141_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413518", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006212_cbc_m1_63_75_m2_63_75_mtotal_127_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413574", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009164_cbc_m1_69_59_m2_7_91_mtotal_77_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413601", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009820_cbc_m1_60_15_m2_11_35_mtotal_71_q_5_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413609", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002738_cbc_m1_12_19_m2_1_31_mtotal_13_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008287_gn_m1_10_16_m2_5_34_mtotal_15_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413641", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008167_gn_m1_25_5_m2_5_0_mtotal_30_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413657", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002421_gn_m1_98_99_m2_11_51_mtotal_110_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413694", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008106_cbc_m1_24_52_m2_5_98_mtotal_30_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413726", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004357_gn_m1_53_03_m2_7_47_mtotal_60_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413725", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007291_gn_m1_20_12_m2_14_38_mtotal_34_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413731", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004154_cbc_m1_130_95_m2_19_55_mtotal_150_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413740", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007889_gn_m1_99_67_m2_49_83_mtotal_149_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413764", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004656_cbc_m1_55_79_m2_50_71_mtotal_106_q_1_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413762", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003803_gn_m1_89_88_m2_17_62_mtotal_107_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413795", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007171_gn_m1_124_79_m2_13_71_mtotal_138_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413807", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004108_cbc_m1_65_35_m2_18_15_mtotal_83_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413852", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009546_cbc_m1_31_02_m2_3_48_mtotal_34_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413883", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009614_cbc_m1_21_51_m2_2_99_mtotal_24_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008003_gn_m1_59_01_m2_6_49_mtotal_65_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413957", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004603_gn_m1_92_17_m2_26_33_mtotal_118_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003361_gn_m1_82_73_m2_21_77_mtotal_104_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002137_gn_m1_18_71_m2_7_79_mtotal_26_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414030", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004624_cbc_m1_8_54_m2_0_96_mtotal_9_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414020", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007623_gn_m1_119_25_m2_13_25_mtotal_132_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007135_gn_m1_123_81_m2_17_69_mtotal_141_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414044", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005514_cbc_m1_94_59_m2_13_91_mtotal_108_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414043", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003558_cbc_m1_53_94_m2_5_56_mtotal_59_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414069", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003857_gn_m1_36_39_m2_10_11_mtotal_46_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414063", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001476_cbc_m1_63_75_m2_18_75_mtotal_82_q_3_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001715_gn_m1_63_88_m2_11_62_mtotal_75_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414083", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003066_cbc_m1_131_81_m2_16_69_mtotal_148_q_7_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414088", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007788_cbc_m1_126_82_m2_12_68_mtotal_139_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414125", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002230_cbc_m1_102_52_m2_26_98_mtotal_129_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414155", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002171_gn_m1_80_09_m2_17_41_mtotal_97_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414123", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006838_cbc_m1_126_36_m2_19_14_mtotal_145_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414096", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009747_gn_m1_80_22_m2_10_28_mtotal_90_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414173", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005039_gn_m1_14_05_m2_1_45_mtotal_15_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414183", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004309_gn_m1_77_57_m2_12_93_mtotal_90_q_6_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414197", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008154_cbc_m1_106_81_m2_16_69_mtotal_123_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414186", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003010_cbc_m1_13_04_m2_1_46_mtotal_14_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414211", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001181_gn_m1_12_83_m2_11_67_mtotal_24_q_1_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414215", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008492_cbc_m1_76_99_m2_13_51_mtotal_90_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414246", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004452_cbc_m1_96_59_m2_43_91_mtotal_140_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414230", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009024_cbc_m1_96_43_m2_16_07_mtotal_112_q_6_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414217", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004399_gn_m1_71_88_m2_15_63_mtotal_87_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414283", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008342_cbc_m1_116_32_m2_18_18_mtotal_134_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414286", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007616_cbc_m1_69_35_m2_14_15_mtotal_83_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009783_gn_m1_49_88_m2_35_62_mtotal_85_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414302", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001929_gn_m1_95_14_m2_41_36_mtotal_136_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003553_gn_m1_18_14_m2_2_36_mtotal_20_q_7_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000109_gn_m1_126_84_m2_15_66_mtotal_142_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414468", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006621_gn_m1_31_86_m2_6_64_mtotal_38_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414435", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002002_cbc_m1_91_05_m2_14_45_mtotal_105_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414471", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006794_cbc_m1_40_38_m2_4_12_mtotal_44_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001245_gn_m1_88_67_m2_15_83_mtotal_104_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414532", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007213_gn_m1_20_11_m2_2_39_mtotal_22_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414529", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004446_cbc_m1_7_56_m2_1_94_mtotal_9_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008705_gn_m1_83_25_m2_11_25_mtotal_94_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414577", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009697_gn_m1_36_26_m2_8_24_mtotal_44_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414584", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005070_cbc_m1_112_14_m2_15_36_mtotal_127_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006179_gn_m1_22_77_m2_3_73_mtotal_26_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414586", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005147_gn_m1_71_59_m2_15_91_mtotal_87_q_4_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414590", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003035_gn_m1_121_93_m2_12_57_mtotal_134_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414580", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005392_cbc_m1_66_27_m2_55_23_mtotal_121_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414606", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007760_cbc_m1_31_64_m2_3_86_mtotal_35_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414617", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007318_cbc_m1_19_97_m2_2_53_mtotal_22_q_7_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414599", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006867_gn_m1_107_11_m2_11_39_mtotal_118_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000570_cbc_m1_85_68_m2_13_82_mtotal_99_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414625", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007964_cbc_m1_91_78_m2_41_72_mtotal_133_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414688", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009311_gn_m1_13_82_m2_1_68_mtotal_15_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414784", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007534_cbc_m1_88_65_m2_9_85_mtotal_98_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414804", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004550_cbc_m1_118_58_m2_28_92_mtotal_147_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414811", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006101_gn_m1_97_76_m2_10_74_mtotal_108_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005218_cbc_m1_75_9_m2_14_6_mtotal_90_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002962_cbc_m1_59_46_m2_8_04_mtotal_67_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414879", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004896_cbc_m1_84_66_m2_9_84_mtotal_94_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007756_cbc_m1_10_45_m2_2_05_mtotal_12_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414848", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009088_cbc_m1_132_81_m2_13_69_mtotal_146_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414889", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008844_cbc_m1_21_24_m2_2_26_mtotal_23_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414878", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006998_cbc_m1_76_75_m2_7_75_mtotal_84_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414826", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000418_cbc_m1_44_83_m2_4_67_mtotal_49_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003239_gn_m1_47_4_m2_5_1_mtotal_52_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414971", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000100_cbc_m1_41_52_m2_12_98_mtotal_54_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415019", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004278_cbc_m1_39_6_m2_9_9_mtotal_49_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415021", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008140_cbc_m1_64_57_m2_17_93_mtotal_82_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415023", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009686_cbc_m1_53_09_m2_10_41_mtotal_63_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000757_gn_m1_29_31_m2_4_19_mtotal_33_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415038", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007263_gn_m1_110_77_m2_12_73_mtotal_123_q_8_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415110", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001460_cbc_m1_79_86_m2_16_64_mtotal_96_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415126", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005112_cbc_m1_28_93_m2_3_57_mtotal_32_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415184", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008082_cbc_m1_53_7_m2_35_8_mtotal_89_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415165", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008043_gn_m1_55_47_m2_6_03_mtotal_61_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007479_gn_m1_97_5_m2_39_0_mtotal_136_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415268", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006912_cbc_m1_16_84_m2_3_66_mtotal_20_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415296", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005948_cbc_m1_115_93_m2_17_57_mtotal_133_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415309", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006139_gn_m1_80_55_m2_8_95_mtotal_89_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415327", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006897_gn_m1_115_85_m2_24_65_mtotal_140_q_4_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415351", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006238_cbc_m1_17_59_m2_1_91_mtotal_19_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415367", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000877_gn_m1_52_5_m2_35_0_mtotal_87_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007599_gn_m1_4_64_m2_1_86_mtotal_6_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415411", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001400_cbc_m1_54_65_m2_18_85_mtotal_73_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415410", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002493_gn_m1_62_04_m2_34_46_mtotal_96_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415416", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004567_gn_m1_15_7_m2_2_8_mtotal_18_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415456", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006779_gn_m1_38_77_m2_4_73_mtotal_43_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415449", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006458_cbc_m1_48_82_m2_40_68_mtotal_89_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415467", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004688_cbc_m1_29_85_m2_7_65_mtotal_37_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415388", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003528_cbc_m1_6_79_m2_2_71_mtotal_9_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415540", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007619_gn_m1_69_35_m2_14_15_mtotal_83_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415584", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004503_gn_m1_41_36_m2_4_14_mtotal_45_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002515_gn_m1_13_7_m2_2_8_mtotal_16_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415607", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004021_gn_m1_69_1_m2_14_4_mtotal_83_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415613", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001351_gn_m1_31_75_m2_31_75_mtotal_63_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415636", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001429_gn_m1_98_33_m2_49_17_mtotal_147_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415665", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001994_cbc_m1_31_41_m2_13_09_mtotal_44_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415673", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008462_cbc_m1_10_21_m2_7_29_mtotal_17_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415687", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000076_cbc_m1_114_14_m2_25_36_mtotal_139_q_4_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008894_cbc_m1_47_13_m2_6_37_mtotal_53_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415705", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002217_gn_m1_7_01_m2_1_49_mtotal_8_q_4_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415733", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001427_gn_m1_21_19_m2_7_31_mtotal_28_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415767", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006452_cbc_m1_111_15_m2_16_35_mtotal_127_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415782", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000648_cbc_m1_71_2_m2_11_3_mtotal_82_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415814", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003564_cbc_m1_101_5_m2_35_0_mtotal_136_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415790", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008993_gn_m1_124_24_m2_17_26_mtotal_141_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415749", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007724_cbc_m1_9_41_m2_4_09_mtotal_13_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415775", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000471_gn_m1_4_55_m2_0_95_mtotal_5_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004203_gn_m1_93_0_m2_46_5_mtotal_139_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415812", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004346_cbc_m1_106_37_m2_13_13_mtotal_119_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415779", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005677_gn_m1_33_75_m2_18_75_mtotal_52_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415818", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009157_gn_m1_19_55_m2_1_95_mtotal_21_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415839", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006692_cbc_m1_51_54_m2_6_96_mtotal_58_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415825", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005471_gn_m1_4_91_m2_2_59_mtotal_7_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415868", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002629_gn_m1_39_51_m2_3_99_mtotal_43_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415874", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006422_cbc_m1_64_02_m2_30_48_mtotal_94_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415966", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004530_cbc_m1_43_15_m2_6_35_mtotal_49_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416002", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006556_cbc_m1_86_49_m2_9_01_mtotal_95_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415984", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000268_cbc_m1_51_69_m2_5_81_mtotal_57_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416025", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002203_gn_m1_91_09_m2_31_41_mtotal_122_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009493_gn_m1_16_93_m2_4_57_mtotal_21_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416024", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000375_gn_m1_35_65_m2_6_85_mtotal_42_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416035", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002376_cbc_m1_26_26_m2_3_24_mtotal_29_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416093", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006836_cbc_m1_126_36_m2_19_14_mtotal_145_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416149", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005628_cbc_m1_49_77_m2_8_73_mtotal_58_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416159", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007702_cbc_m1_51_25_m2_6_25_mtotal_57_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416163", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000053_gn_m1_22_6_m2_11_9_mtotal_34_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416215", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003387_gn_m1_66_5_m2_19_0_mtotal_85_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416255", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002053_gn_m1_66_29_m2_9_21_mtotal_75_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416379", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002272_cbc_m1_103_04_m2_17_46_mtotal_120_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416382", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004478_cbc_m1_115_86_m2_19_64_mtotal_135_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416368", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002470_cbc_m1_32_45_m2_27_05_mtotal_59_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416393", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008781_gn_m1_6_87_m2_1_63_mtotal_8_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416404", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007487_gn_m1_40_95_m2_21_55_mtotal_62_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416396", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006255_gn_m1_6_07_m2_2_43_mtotal_8_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416517", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007689_gn_m1_41_46_m2_23_04_mtotal_64_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416553", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008538_cbc_m1_86_54_m2_13_96_mtotal_100_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416564", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000900_cbc_m1_77_43_m2_8_07_mtotal_85_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416476", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003853_gn_m1_36_39_m2_10_11_mtotal_46_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416527", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003253_gn_m1_40_09_m2_33_41_mtotal_73_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416516", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005311_gn_m1_103_73_m2_35_77_mtotal_139_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416573", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007292_cbc_m1_20_12_m2_14_38_mtotal_34_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005857_gn_m1_92_49_m2_11_01_mtotal_103_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416417", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000476_cbc_m1_6_27_m2_5_23_mtotal_11_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416703", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003342_cbc_m1_15_62_m2_4_88_mtotal_20_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416627", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003526_cbc_m1_93_74_m2_40_76_mtotal_134_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416745", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001416_cbc_m1_31_35_m2_6_15_mtotal_37_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416772", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004164_cbc_m1_26_14_m2_11_36_mtotal_37_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416819", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007103_gn_m1_99_35_m2_26_15_mtotal_125_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416821", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002510_cbc_m1_95_71_m2_16_79_mtotal_112_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003013_gn_m1_72_97_m2_15_53_mtotal_88_q_4_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005506_cbc_m1_120_75_m2_28_75_mtotal_149_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002925_gn_m1_67_4_m2_32_1_mtotal_99_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416864", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007238_cbc_m1_50_47_m2_24_03_mtotal_74_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005227_gn_m1_75_55_m2_10_95_mtotal_86_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416983", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001521_gn_m1_110_9_m2_12_6_mtotal_123_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416992", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006314_cbc_m1_102_82_m2_11_68_mtotal_114_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417011", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009393_gn_m1_53_94_m2_8_56_mtotal_62_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417029", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002733_gn_m1_18_38_m2_6_12_mtotal_24_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417013", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000943_gn_m1_50_42_m2_10_08_mtotal_60_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417041", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003325_gn_m1_95_56_m2_11_94_mtotal_107_q_8_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417074", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000749_gn_m1_78_93_m2_31_57_mtotal_110_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417053", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000644_cbc_m1_45_65_m2_19_85_mtotal_65_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417024", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005590_cbc_m1_47_2_m2_14_3_mtotal_61_q_3_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417100", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000382_cbc_m1_96_31_m2_13_19_mtotal_109_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417114", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007965_gn_m1_91_78_m2_41_72_mtotal_133_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004062_cbc_m1_127_72_m2_18_78_mtotal_146_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417153", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000735_gn_m1_22_92_m2_2_58_mtotal_25_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417170", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009051_gn_m1_109_86_m2_16_64_mtotal_126_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417213", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008028_cbc_m1_11_53_m2_3_97_mtotal_15_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007100_cbc_m1_99_35_m2_26_15_mtotal_125_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417261", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002874_cbc_m1_99_11_m2_13_39_mtotal_112_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417287", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001168_cbc_m1_75_69_m2_68_81_mtotal_144_q_1_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417374", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008581_gn_m1_58_5_m2_9_0_mtotal_67_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003772_cbc_m1_25_76_m2_10_74_mtotal_36_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417379", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004549_gn_m1_118_58_m2_28_92_mtotal_147_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417393", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009830_cbc_m1_5_81_m2_0_69_mtotal_6_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006106_cbc_m1_36_5_m2_5_0_mtotal_41_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417429", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000210_cbc_m1_93_19_m2_13_31_mtotal_106_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417432", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008016_cbc_m1_5_25_m2_5_25_mtotal_10_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417411", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006132_cbc_m1_106_99_m2_34_51_mtotal_141_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417464", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004494_cbc_m1_19_46_m2_3_04_mtotal_22_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004101_gn_m1_79_9_m2_21_6_mtotal_101_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417503", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008037_gn_m1_6_48_m2_2_02_mtotal_8_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417541", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001536_cbc_m1_127_66_m2_14_84_mtotal_142_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006723_gn_m1_18_91_m2_2_59_mtotal_21_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417577", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008120_cbc_m1_134_98_m2_15_52_mtotal_150_q_8_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417590", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009324_cbc_m1_125_65_m2_22_85_mtotal_148_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009360_cbc_m1_62_01_m2_8_49_mtotal_70_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417571", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001685_gn_m1_72_62_m2_51_88_mtotal_124_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417612", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003139_gn_m1_86_32_m2_9_18_mtotal_95_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417607", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001661_gn_m1_100_37_m2_27_13_mtotal_127_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417623", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003734_cbc_m1_26_9_m2_5_6_mtotal_32_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007981_gn_m1_41_25_m2_6_25_mtotal_47_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003106_cbc_m1_62_49_m2_8_01_mtotal_70_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009885_gn_m1_71_16_m2_32_34_mtotal_103_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417690", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004721_gn_m1_68_9_m2_10_6_mtotal_79_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007654_cbc_m1_55_48_m2_7_02_mtotal_62_q_7_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417728", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001632_cbc_m1_19_21_m2_2_29_mtotal_21_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417742", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005389_gn_m1_66_27_m2_55_23_mtotal_121_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417740", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006580_cbc_m1_26_69_m2_2_81_mtotal_29_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417696", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002794_cbc_m1_69_53_m2_23_97_mtotal_93_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417806", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008468_cbc_m1_107_95_m2_38_55_mtotal_146_q_2_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417805", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008818_cbc_m1_60_82_m2_6_68_mtotal_67_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417808", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002323_gn_m1_49_5_m2_5_0_mtotal_54_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417830", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009596_cbc_m1_34_01_m2_5_49_mtotal_39_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417936", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002724_cbc_m1_85_58_m2_8_92_mtotal_94_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417856", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006694_cbc_m1_51_54_m2_6_96_mtotal_58_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417971", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005320_cbc_m1_46_27_m2_7_23_mtotal_53_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002995_gn_m1_38_21_m2_27_29_mtotal_65_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418076", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008717_gn_m1_84_91_m2_38_59_mtotal_123_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418086", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002716_cbc_m1_107_83_m2_32_67_mtotal_140_q_3_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418060", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007924_cbc_m1_82_61_m2_15_89_mtotal_98_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418092", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004876_cbc_m1_43_75_m2_4_75_mtotal_48_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418121", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006539_gn_m1_44_19_m2_6_31_mtotal_50_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000214_cbc_m1_93_19_m2_13_31_mtotal_106_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418168", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009330_cbc_m1_83_18_m2_8_32_mtotal_91_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418147", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009695_gn_m1_10_25_m2_10_25_mtotal_20_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418146", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009942_cbc_m1_34_57_m2_3_93_mtotal_38_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418175", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000895_gn_m1_64_79_m2_7_71_mtotal_72_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418177", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000574_cbc_m1_85_68_m2_13_82_mtotal_99_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418192", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003141_gn_m1_86_32_m2_9_18_mtotal_95_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418186", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000158_cbc_m1_70_57_m2_7_93_mtotal_78_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418190", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008928_cbc_m1_16_38_m2_7_12_mtotal_23_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418189", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001538_cbc_m1_127_66_m2_14_84_mtotal_142_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418179", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005986_cbc_m1_66_81_m2_25_69_mtotal_92_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418208", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001428_cbc_m1_98_33_m2_49_17_mtotal_147_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418191", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005950_cbc_m1_115_93_m2_17_57_mtotal_133_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418195", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005127_gn_m1_99_27_m2_10_23_mtotal_109_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418224", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007764_cbc_m1_19_58_m2_3_92_mtotal_23_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418239", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007862_cbc_m1_42_5_m2_25_0_mtotal_67_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418242", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009103_gn_m1_85_78_m2_10_72_mtotal_96_q_8_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009261_gn_m1_105_88_m2_24_62_mtotal_130_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418230", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003908_cbc_m1_103_53_m2_16_97_mtotal_120_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418288", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009572_cbc_m1_88_83_m2_13_67_mtotal_102_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418314", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002423_gn_m1_98_99_m2_11_51_mtotal_110_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418327", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001126_cbc_m1_74_79_m2_9_71_mtotal_84_q_7_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418400", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001269_gn_m1_57_0_m2_28_5_mtotal_85_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418407", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009397_gn_m1_64_54_m2_8_96_mtotal_73_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418410", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007531_gn_m1_88_65_m2_9_85_mtotal_98_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418425", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007904_cbc_m1_12_89_m2_4_61_mtotal_17_q_2_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418417", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004156_cbc_m1_130_95_m2_19_55_mtotal_150_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006577_gn_m1_26_69_m2_2_81_mtotal_29_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418530", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004026_cbc_m1_39_35_m2_23_15_mtotal_62_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418468", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000023_gn_m1_26_76_m2_15_74_mtotal_42_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418532", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006802_cbc_m1_132_37_m2_18_13_mtotal_150_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418490", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005188_cbc_m1_15_82_m2_3_68_mtotal_19_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418516", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007631_gn_m1_118_36_m2_21_14_mtotal_139_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418519", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002970_cbc_m1_28_58_m2_2_92_mtotal_31_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418466", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001253_gn_m1_31_37_m2_7_13_mtotal_38_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418553", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002826_cbc_m1_24_88_m2_2_62_mtotal_27_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001665_gn_m1_7_42_m2_1_08_mtotal_8_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418581", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004923_gn_m1_34_12_m2_11_38_mtotal_45_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418621", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005373_gn_m1_28_28_m2_4_22_mtotal_32_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418635", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005508_cbc_m1_15_43_m2_9_07_mtotal_24_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418633", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009822_cbc_m1_17_15_m2_2_35_mtotal_19_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005125_gn_m1_99_27_m2_10_23_mtotal_109_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003286_cbc_m1_66_69_m2_9_81_mtotal_76_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418718", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000879_gn_m1_52_5_m2_35_0_mtotal_87_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418727", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003561_gn_m1_53_94_m2_5_56_mtotal_59_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418732", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003172_cbc_m1_83_76_m2_9_74_mtotal_93_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001962_cbc_m1_102_08_m2_20_42_mtotal_122_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001931_gn_m1_95_14_m2_41_36_mtotal_136_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418811", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007680_cbc_m1_62_33_m2_14_17_mtotal_76_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418760", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000541_gn_m1_24_73_m2_11_77_mtotal_36_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418914", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005917_gn_m1_22_6_m2_2_9_mtotal_25_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418934", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001190_cbc_m1_90_04_m2_20_46_mtotal_110_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418989", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009166_cbc_m1_69_59_m2_7_91_mtotal_77_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005305_gn_m1_129_21_m2_19_29_mtotal_148_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006865_gn_m1_107_11_m2_11_39_mtotal_118_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419039", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002863_gn_m1_31_97_m2_6_53_mtotal_38_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419111", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006134_cbc_m1_106_99_m2_34_51_mtotal_141_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419129", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001305_gn_m1_7_12_m2_2_38_mtotal_9_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419137", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007920_cbc_m1_82_61_m2_15_89_mtotal_98_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419159", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004732_cbc_m1_121_77_m2_18_73_mtotal_140_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419173", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003850_cbc_m1_61_58_m2_8_92_mtotal_70_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419176", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001734_cbc_m1_57_67_m2_5_83_mtotal_63_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419187", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009180_cbc_m1_53_8_m2_11_7_mtotal_65_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419192", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002193_gn_m1_87_95_m2_22_55_mtotal_110_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006039_gn_m1_91_46_m2_16_04_mtotal_107_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419189", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008569_gn_m1_55_11_m2_42_39_mtotal_97_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419229", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000873_gn_m1_61_75_m2_23_75_mtotal_85_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419240", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004392_cbc_m1_31_42_m2_12_08_mtotal_43_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419253", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006735_gn_m1_80_94_m2_11_56_mtotal_92_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419256", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009289_gn_m1_6_38_m2_1_12_mtotal_7_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419242", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005882_cbc_m1_69_91_m2_10_59_mtotal_80_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419278", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004915_gn_m1_107_25_m2_41_25_mtotal_148_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419284", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001804_cbc_m1_57_79_m2_7_71_mtotal_65_q_7_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419349", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005473_gn_m1_5_89_m2_0_61_mtotal_6_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419290", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001682_cbc_m1_72_62_m2_51_88_mtotal_124_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419399", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002258_cbc_m1_62_48_m2_13_02_mtotal_75_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419413", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005600_cbc_m1_79_6_m2_10_9_mtotal_90_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419441", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003394_cbc_m1_9_41_m2_1_09_mtotal_10_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419449", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003463_gn_m1_50_17_m2_14_33_mtotal_64_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419514", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008112_cbc_m1_38_18_m2_12_32_mtotal_50_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419502", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000717_gn_m1_125_56_m2_12_94_mtotal_138_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419522", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002742_cbc_m1_93_91_m2_11_59_mtotal_105_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419521", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008867_gn_m1_87_94_m2_12_56_mtotal_100_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419511", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004928_cbc_m1_44_92_m2_4_58_mtotal_49_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419530", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006844_cbc_m1_116_02_m2_12_48_mtotal_128_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009827_gn_m1_17_15_m2_2_35_mtotal_19_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419540", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006926_cbc_m1_22_6_m2_3_9_mtotal_26_q_5_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419601", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000491_gn_m1_97_77_m2_25_73_mtotal_123_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009882_cbc_m1_71_16_m2_32_34_mtotal_103_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419619", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004806_cbc_m1_54_26_m2_10_24_mtotal_64_q_5_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419568", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001281_gn_m1_51_38_m2_32_12_mtotal_83_q_1_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419623", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008853_gn_m1_65_6_m2_6_9_mtotal_72_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419629", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002633_gn_m1_39_51_m2_3_99_mtotal_43_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419690", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004605_gn_m1_92_17_m2_26_33_mtotal_118_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001884_cbc_m1_86_04_m2_9_46_mtotal_95_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009291_gn_m1_6_38_m2_1_12_mtotal_7_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419699", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001919_gn_m1_101_58_m2_19_92_mtotal_121_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419713", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005844_cbc_m1_44_06_m2_5_44_mtotal_49_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002745_gn_m1_93_91_m2_11_59_mtotal_105_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419662", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002568_cbc_m1_126_23_m2_24_27_mtotal_150_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419734", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008992_cbc_m1_124_24_m2_17_26_mtotal_141_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419779", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006751_gn_m1_111_14_m2_17_36_mtotal_128_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419797", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002257_gn_m1_62_48_m2_13_02_mtotal_75_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419834", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002211_gn_m1_48_44_m2_23_06_mtotal_71_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419812", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004653_gn_m1_26_54_m2_18_96_mtotal_45_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419852", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009508_cbc_m1_94_58_m2_18_92_mtotal_113_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419850", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006546_cbc_m1_23_23_m2_3_27_mtotal_26_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419860", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004824_cbc_m1_27_12_m2_19_38_mtotal_46_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419854", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003631_gn_m1_27_78_m2_7_72_mtotal_35_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419855", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006687_gn_m1_29_75_m2_8_75_mtotal_38_q_3_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419882", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002023_gn_m1_104_56_m2_13_94_mtotal_118_q_7_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419858", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008300_cbc_m1_36_44_m2_5_06_mtotal_41_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419920", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000293_gn_m1_88_3_m2_19_2_mtotal_107_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419968", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006368_cbc_m1_59_32_m2_21_18_mtotal_80_q_2_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420008", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005974_cbc_m1_30_62_m2_21_88_mtotal_52_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004560_cbc_m1_21_72_m2_12_78_mtotal_34_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420017", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002305_gn_m1_71_15_m2_17_35_mtotal_88_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419993", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003714_cbc_m1_29_84_m2_4_66_mtotal_34_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420070", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006524_cbc_m1_109_74_m2_12_76_mtotal_122_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420106", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000567_gn_m1_71_09_m2_7_41_mtotal_78_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420088", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001865_gn_m1_49_36_m2_41_14_mtotal_90_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420040", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006595_gn_m1_77_3_m2_11_2_mtotal_88_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420134", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004572_cbc_m1_37_2_m2_9_3_mtotal_46_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003191_gn_m1_21_39_m2_4_11_mtotal_25_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420141", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002521_gn_m1_27_08_m2_5_42_mtotal_32_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008643_gn_m1_25_88_m2_13_62_mtotal_39_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420280", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008177_gn_m1_77_48_m2_18_02_mtotal_95_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000767_gn_m1_50_16_m2_5_34_mtotal_55_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420247", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001157_gn_m1_14_47_m2_6_03_mtotal_20_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002284_cbc_m1_108_4_m2_27_1_mtotal_135_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420271", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000815_gn_m1_69_31_m2_10_19_mtotal_79_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420238", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001121_gn_m1_59_11_m2_17_39_mtotal_76_q_3_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420364", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009529_gn_m1_97_67_m2_48_83_mtotal_146_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009643_gn_m1_52_18_m2_19_32_mtotal_71_q_2_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420304", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007000_cbc_m1_76_75_m2_7_75_mtotal_84_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420375", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003298_cbc_m1_37_59_m2_28_91_mtotal_66_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420328", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002615_gn_m1_115_27_m2_16_23_mtotal_131_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420411", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009003_gn_m1_47_79_m2_7_71_mtotal_55_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420435", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004127_gn_m1_65_36_m2_26_14_mtotal_91_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420468", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009555_gn_m1_104_93_m2_20_57_mtotal_125_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420477", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005756_cbc_m1_71_23_m2_7_27_mtotal_78_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420511", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009564_cbc_m1_34_24_m2_9_26_mtotal_43_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420525", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004401_gn_m1_71_88_m2_15_63_mtotal_87_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420572", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001706_cbc_m1_91_91_m2_14_59_mtotal_106_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008269_gn_m1_96_96_m2_10_54_mtotal_107_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420576", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000492_cbc_m1_124_16_m2_24_34_mtotal_148_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420563", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001900_cbc_m1_71_75_m2_71_75_mtotal_143_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420588", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006604_cbc_m1_78_72_m2_10_78_mtotal_89_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420591", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009336_cbc_m1_35_7_m2_23_8_mtotal_59_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004178_cbc_m1_46_28_m2_27_22_mtotal_73_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420619", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003836_cbc_m1_61_35_m2_21_15_mtotal_82_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420654", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002734_cbc_m1_18_38_m2_6_12_mtotal_24_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420633", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002519_gn_m1_13_7_m2_2_8_mtotal_16_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005171_gn_m1_57_44_m2_13_06_mtotal_70_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420698", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002990_cbc_m1_123_28_m2_15_22_mtotal_138_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420663", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009334_cbc_m1_83_18_m2_8_32_mtotal_91_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420773", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002627_gn_m1_46_12_m2_15_38_mtotal_61_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420754", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002657_gn_m1_14_41_m2_2_09_mtotal_16_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420737", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002042_cbc_m1_61_85_m2_6_65_mtotal_68_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420819", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007967_gn_m1_91_78_m2_41_72_mtotal_133_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420715", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005267_gn_m1_27_5_m2_5_0_mtotal_32_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420826", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004889_gn_m1_68_0_m2_8_5_mtotal_76_q_8_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420840", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003022_cbc_m1_39_19_m2_10_31_mtotal_49_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420936", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004905_gn_m1_21_76_m2_16_74_mtotal_38_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421005", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000598_cbc_m1_78_8_m2_19_7_mtotal_98_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421030", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009317_gn_m1_46_5_m2_31_0_mtotal_77_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421057", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006049_gn_m1_5_67_m2_1_83_mtotal_7_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004052_cbc_m1_96_11_m2_53_39_mtotal_149_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421055", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008428_cbc_m1_70_92_m2_10_58_mtotal_81_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421058", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004622_cbc_m1_8_54_m2_0_96_mtotal_9_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421069", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001482_cbc_m1_112_62_m2_17_88_mtotal_130_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009567_gn_m1_34_24_m2_9_26_mtotal_43_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421094", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005730_cbc_m1_14_88_m2_1_62_mtotal_16_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421137", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002400_cbc_m1_45_5_m2_7_0_mtotal_52_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006943_gn_m1_24_66_m2_2_84_mtotal_27_q_8_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421206", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008764_cbc_m1_19_71_m2_6_79_mtotal_26_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421195", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009658_cbc_m1_67_98_m2_11_52_mtotal_79_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421178", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004526_cbc_m1_21_66_m2_9_84_mtotal_31_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001985_gn_m1_118_04_m2_21_46_mtotal_139_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421241", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008765_gn_m1_19_71_m2_6_79_mtotal_26_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421232", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007951_gn_m1_37_86_m2_6_64_mtotal_44_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007462_cbc_m1_60_81_m2_11_69_mtotal_72_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421257", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002771_gn_m1_20_79_m2_6_71_mtotal_27_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421287", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004174_cbc_m1_74_89_m2_41_61_mtotal_116_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421303", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002036_cbc_m1_68_31_m2_10_19_mtotal_78_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421323", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007356_cbc_m1_75_54_m2_53_96_mtotal_129_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421346", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004833_gn_m1_83_61_m2_23_89_mtotal_107_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421347", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002598_cbc_m1_107_96_m2_24_54_mtotal_132_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421345", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004039_gn_m1_56_12_m2_8_38_mtotal_64_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421364", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005697_gn_m1_15_61_m2_2_89_mtotal_18_q_5_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421377", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005793_gn_m1_79_92_m2_11_58_mtotal_91_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003312_cbc_m1_41_71_m2_29_79_mtotal_71_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421431", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004354_cbc_m1_23_32_m2_5_18_mtotal_28_q_4_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421477", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004095_gn_m1_44_17_m2_11_33_mtotal_55_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421521", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001591_gn_m1_50_38_m2_8_12_mtotal_58_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421574", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001724_cbc_m1_30_42_m2_6_08_mtotal_36_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421580", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009396_cbc_m1_64_54_m2_8_96_mtotal_73_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421522", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004640_cbc_m1_94_82_m2_52_68_mtotal_147_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421607", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004980_cbc_m1_44_68_m2_24_82_mtotal_69_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000224_cbc_m1_83_52_m2_8_98_mtotal_92_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421640", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008735_gn_m1_58_29_m2_8_21_mtotal_66_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421705", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006890_cbc_m1_6_59_m2_0_91_mtotal_7_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009281_gn_m1_55_12_m2_39_38_mtotal_94_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003212_cbc_m1_50_54_m2_12_96_mtotal_63_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421792", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008934_cbc_m1_107_5_m2_25_0_mtotal_132_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421797", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006782_cbc_m1_67_71_m2_8_79_mtotal_76_q_7_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006326_cbc_m1_131_92_m2_18_58_mtotal_150_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421872", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007022_cbc_m1_13_75_m2_13_75_mtotal_27_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421893", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008653_gn_m1_116_4_m2_29_1_mtotal_145_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421961", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004424_cbc_m1_5_52_m2_0_98_mtotal_6_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421958", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009744_cbc_m1_80_22_m2_10_28_mtotal_90_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421927", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008284_cbc_m1_87_18_m2_36_32_mtotal_123_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002764_cbc_m1_82_92_m2_16_58_mtotal_99_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422025", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004764_cbc_m1_13_61_m2_3_89_mtotal_17_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422048", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001326_cbc_m1_82_51_m2_13_99_mtotal_96_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422130", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000871_gn_m1_61_75_m2_23_75_mtotal_85_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422109", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008555_gn_m1_15_47_m2_3_03_mtotal_18_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422165", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009405_gn_m1_50_29_m2_6_21_mtotal_56_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422180", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000501_gn_m1_94_12_m2_12_38_mtotal_106_q_7_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422161", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002535_gn_m1_71_94_m2_20_56_mtotal_92_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422233", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009386_cbc_m1_87_22_m2_12_28_mtotal_99_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422256", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009049_gn_m1_109_86_m2_16_64_mtotal_126_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422255", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005935_gn_m1_74_71_m2_17_79_mtotal_92_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422271", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003335_gn_m1_49_06_m2_9_44_mtotal_58_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422339", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003303_gn_m1_9_0_m2_4_5_mtotal_13_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005470_cbc_m1_4_91_m2_2_59_mtotal_7_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422371", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003462_cbc_m1_50_17_m2_14_33_mtotal_64_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422377", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003036_cbc_m1_96_06_m2_10_44_mtotal_106_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422430", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000334_cbc_m1_119_64_m2_12_86_mtotal_132_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422391", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004546_cbc_m1_85_0_m2_42_5_mtotal_127_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422392", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007444_cbc_m1_31_88_m2_10_62_mtotal_42_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422437", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002649_gn_m1_64_82_m2_6_68_mtotal_71_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422454", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001136_cbc_m1_85_66_m2_17_84_mtotal_103_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000078_cbc_m1_55_96_m2_15_54_mtotal_71_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422581", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002364_cbc_m1_65_25_m2_65_25_mtotal_130_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422622", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007015_gn_m1_60_97_m2_9_53_mtotal_70_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422644", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006679_gn_m1_111_07_m2_15_43_mtotal_126_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007495_gn_m1_55_6_m2_13_9_mtotal_69_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422693", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004335_gn_m1_52_95_m2_16_55_mtotal_69_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422753", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009028_cbc_m1_96_43_m2_16_07_mtotal_112_q_6_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422657", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007869_gn_m1_12_46_m2_3_04_mtotal_15_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007396_cbc_m1_58_11_m2_6_39_mtotal_64_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422771", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001236_cbc_m1_42_07_m2_4_43_mtotal_46_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422750", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004281_gn_m1_39_6_m2_9_9_mtotal_49_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422788", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005606_cbc_m1_85_67_m2_42_83_mtotal_128_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422756", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000639_gn_m1_72_92_m2_8_58_mtotal_81_q_8_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422820", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007901_gn_m1_96_4_m2_24_1_mtotal_120_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422863", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003849_gn_m1_61_58_m2_8_92_mtotal_70_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422872", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008264_cbc_m1_22_09_m2_18_41_mtotal_40_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007540_cbc_m1_67_34_m2_7_16_mtotal_74_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422916", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001277_gn_m1_21_0_m2_10_5_mtotal_31_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422934", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004291_gn_m1_94_24_m2_39_26_mtotal_133_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006265_gn_m1_46_38_m2_33_12_mtotal_79_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422952", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000317_gn_m1_109_41_m2_16_09_mtotal_125_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422870", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005978_cbc_m1_120_19_m2_29_31_mtotal_149_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422999", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005721_gn_m1_92_96_m2_17_54_mtotal_110_q_5_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422983", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006762_cbc_m1_95_65_m2_30_85_mtotal_126_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_29991", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5351171982585894144.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_29995", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220740.76+510829.9.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30023", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J041121.05+232307.9.csv", "length": 244, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30057", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53825177.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30052", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10155603.csv", "length": 196, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30049", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J234224.73+510902.1.csv", "length": 298, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30097", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10337599.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30129", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35309866.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30143", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J215859.66+803908.0.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30114", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J100801.83-382802.1.csv", "length": 753, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30138", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32990942.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30156", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220343.31+145103.3.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30162", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59494552.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30166", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J182220.83+184103.1.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30184", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183504.08+375256.8.csv", "length": 530, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30188", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32984207.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30200", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5369485929273451520.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30203", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP44512765.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30210", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31677198.csv", "length": 149, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J212952.70-011018.4.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30250", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18897382.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30284", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP27756175.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30291", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51869525.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30297", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17666137.csv", "length": 230, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30308", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220346.45+202536.8.csv", "length": 64, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30322", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190326.20+404904.4.csv", "length": 81, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30332", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ164151.76+552347.5.csv", "length": 227, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30328", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28618601.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30347", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190657.05+001715.7.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29499387.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30348", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192548.90+384443.5.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30324", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J125940.64-435637.1.csv", "length": 1088, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30389", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5304897072906526464.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30382", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6725526088089709312.csv", "length": 37, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30401", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225237.01+643230.1.csv", "length": 161, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30396", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29727544.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191018.23+012707.3.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30402", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194338.87+511043.4.csv", "length": 290, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30405", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25396674.csv", "length": 315, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30415", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10103360.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30421", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51859200.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30466", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP11076764.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30520", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ175557.63+433800.7.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30548", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ060206.75+081724.5.csv", "length": 86, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30550", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35038475.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30546", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32972676.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30562", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J062403.24-342502.3.csv", "length": 300, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10478281.csv", "length": 124, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30592", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191442.20+060024.4.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30600", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30031940.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30604", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30938369.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30602", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP575328.csv", "length": 244, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30621", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201134.44+371013.5.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30637", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21642154.csv", "length": 266, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30624", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190240.18+215238.3.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP827900.csv", "length": 112, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30663", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223400.86+220543.1.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30660", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29012634.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30676", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3503715955074017920.csv", "length": 53, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30669", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29390096.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30666", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29816381.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30689", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195242.59-054718.5.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30707", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29437475.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30723", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193834.87+141856.1.csv", "length": 130, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30727", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32799613.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30733", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192633.35+482533.1.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30735", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46578450.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30760", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP60040441.csv", "length": 219, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30774", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28750530.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30785", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51895686.csv", "length": 299, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30818", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200509.43+415657.6.csv", "length": 177, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192904.43+211243.5.csv", "length": 309, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30845", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ153734.67+153503.4.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31144289.csv", "length": 200, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30841", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29473229.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055053.58+303945.4.csv", "length": 105, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30852", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46709079.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5509635586917643264.csv", "length": 40, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30871", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13788159.csv", "length": 292, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30886", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59380890.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51609770.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30873", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35139854.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30905", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ170746.31+752421.2.csv", "length": 112, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30904", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193458.97+163150.9.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30913", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56398344.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30923", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32694621.csv", "length": 432, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30929", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182704.96+505442.9.csv", "length": 265, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30948", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35178185.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30950", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31036290.csv", "length": 197, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30968", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ121535.31+323538.0.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192430.34-000557.0.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30994", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10074949.csv", "length": 196, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31006", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185948.41+294939.0.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20173689.csv", "length": 308, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34714519.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31055", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ122635.70+082306.1.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31065", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ033621.54+604145.3.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31074", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51731066.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31072", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51899770.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31089", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24889508.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31091", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ160837.45+550603.8.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31104", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51821805.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31117", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10194947.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31127", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201137.74+532354.2.csv", "length": 78, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31142", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230012.73+561650.3.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31131", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51652173.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31138", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30932417.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31168", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183641.54+172347.6.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31159", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215604.62+483824.8.csv", "length": 138, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31152", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9627586.csv", "length": 527, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31184", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10410906.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31177", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203420.61+340540.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31185", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58787741.csv", "length": 473, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP42614647.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31222", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5922366550901970304.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31234", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190241.34+214305.4.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31219", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59344850.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31266", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4658656949042309504.csv", "length": 40, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31261", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ062926.11+212311.2.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31297", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35092564.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31292", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221228.97+610649.4.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31317", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202017.30-120338.8.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31335", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J202845.07+050818.4.csv", "length": 269, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31336", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29731283.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31345", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29487267.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31354", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ062713.04+030659.9.csv", "length": 83, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31353", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30046178.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31364", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10409784.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31365", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J023414.27+421427.9.csv", "length": 319, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31381", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1886146952603466624.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53915244.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31389", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32615659.csv", "length": 75, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31395", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10388701.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31405", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J212631.06+355934.3.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31424", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200520.52+320415.1.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31423", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015313.27+564121.8.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31426", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5358762358011232896.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31437", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56860722.csv", "length": 235, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31452", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223553.70+393827.6.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32832943.csv", "length": 253, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28862115.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31496", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214719.96+512632.1.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31500", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015355.14+585951.4.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31515", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ002905.42+513552.9.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31533", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J223043.62+334424.9.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10124474.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31536", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53978791.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31543", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193918.25+103927.3.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31566", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13783373.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31565", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185157.99+364903.0.csv", "length": 277, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31586", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32931775.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31579", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015415.28+633117.1.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31592", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J190719.99-122722.1.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31584", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59334024.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31603", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31706174.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31596", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46021080.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31611", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015256.99+322852.4.csv", "length": 90, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39850546.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31643", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1975051538689813888.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ164706.89+182344.9.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31625", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202556.87+331200.7.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31080406.csv", "length": 88, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31649", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222616.84+611710.3.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31659", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5411388145606855296.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31671", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59994946.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31661", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191314.90+135801.2.csv", "length": 290, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31660", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195050.98+302549.4.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29533331.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46581392.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31703", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29982312.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31710", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230854.66+575355.0.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31709", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ011228.95+681504.4.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31719", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51802456.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31721", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4316998661514305152.csv", "length": 41, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31728", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5890290949210440320.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31731", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30203255.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31735", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32979749.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31739", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13943780.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31750", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J070017.43-174556.9.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31753", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192552.53+021916.5.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31759", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193139.77+080735.9.csv", "length": 172, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31762", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59508988.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31773", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56824274.csv", "length": 260, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31793", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35377682.csv", "length": 204, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31791", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28890692.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31820", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13703696.csv", "length": 343, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31844", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10430678.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31836", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ160919.76+434423.7.csv", "length": 271, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31859", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031533.73+602308.3.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31867", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51848919.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31895", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194250.13+581917.4.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31908", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ175905.64+342119.0.csv", "length": 248, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31914", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25307671.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31930", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35261395.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31941", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201853.29+390559.3.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31963", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225522.87+591315.4.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31962", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35203347.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31971", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32922192.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31967", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224322.08+571226.9.csv", "length": 306, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31994", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32921157.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31991", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183234.45-652253.9.csv", "length": 527, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32005", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J203914.51-225525.8.csv", "length": 426, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32011", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35322300.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32032", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29893434.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32039", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29883357.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29001763.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32065", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32870996.csv", "length": 77, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32074", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ012939.71+602400.8.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32053", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19141376.csv", "length": 260, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32091", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29852195.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32093", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59422551.csv", "length": 313, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32135", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10533008.csv", "length": 312, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32139", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J185417.57+331128.1.csv", "length": 347, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32146", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J205417.62+554904.0.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32150", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ001033.49+584250.9.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32151", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10377354.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32154", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29601703.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32180", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28760062.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32196", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180421.04+440020.8.csv", "length": 258, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32201", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28990473.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32206", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191009.64+025042.5.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32204", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195310.07+215305.8.csv", "length": 155, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32215", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ231038.19+575957.8.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32223", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192605.48+253319.1.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32212", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J172601.57+032240.6.csv", "length": 440, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32250", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200348.65+371535.3.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32945042.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32295", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34620292.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32311", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29533610.csv", "length": 54, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32326", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215359.27+593012.1.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32331", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193447.54+481047.6.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32344", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID509360890554910336.csv", "length": 114, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32352", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ112921.60+281619.6.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32365", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34705704.csv", "length": 150, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32348", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51634184.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32368", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17265605.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32395", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46512201.csv", "length": 265, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32410", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29857848.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32404", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J030925.22+233449.0.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32430", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP14106516.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32461", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055502.51+192557.7.csv", "length": 95, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J113645.09-043120.9.csv", "length": 420, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32469", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53279574.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32482", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202653.23+494315.5.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59377718.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32505", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185557.13-030839.5.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32510", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J041943.98+011559.2.csv", "length": 480, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205736.06+382406.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32535", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46756572.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32540", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ042404.21+451544.4.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32530", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51847171.csv", "length": 401, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190015.26+060715.3.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32547", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024027.69+520814.3.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35190572.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32561", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230214.00+592333.5.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32566", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10465072.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35042758.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32571", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9820720.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32586", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ175712.14+115838.3.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32583", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9539390.csv", "length": 203, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32601", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203612.28+354131.9.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32602", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53809393.csv", "length": 350, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32617", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP44540687.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32612", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222501.59+633252.5.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32613", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ020855.84+554002.7.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055242.57+171114.8.csv", "length": 94, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32624", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32944737.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32639", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35343437.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32643", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J123415.25+481307.1.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32627", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20174648.csv", "length": 317, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32649", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP49767256.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32663", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ035435.02+354223.4.csv", "length": 117, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46045212.csv", "length": 297, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32677", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ233256.90+554843.8.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32687", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9534533.csv", "length": 410, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32699", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192544.45+211955.1.csv", "length": 115, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ231353.50+542251.7.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32731", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19106924.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32782", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10992050.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32759", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29989350.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32796", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195347.73+171117.2.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32798", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ233345.72+435049.6.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32790", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214916.90+494356.3.csv", "length": 318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32797", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32907911.csv", "length": 183, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32804", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2995772850405288576.csv", "length": 43, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32818", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194513.42+175828.1.csv", "length": 105, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32833", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10174553.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32843", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ023031.97+605343.4.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34990174.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32875", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28964536.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32904", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35019426.csv", "length": 95, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32897", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51715274.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32898", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19498951.csv", "length": 300, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32899", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195936.84+365219.3.csv", "length": 428, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ050824.48+300923.2.csv", "length": 114, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32934", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51887340.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32932", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59373781.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32959", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ001740.40+613603.4.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32968", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214208.32+574048.3.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32965", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24607803.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32964", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J135533.61+170747.2.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32695936.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32995", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ174935.23+521802.4.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33024", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39145088.csv", "length": 190, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33045", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24811385.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33038", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29543954.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33036", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP48333565.csv", "length": 180, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33046", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5309561789656283136.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33052", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35054215.csv", "length": 399, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33056", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29892882.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33075", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28936402.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33073", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ014733.61+611329.0.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33084", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35034755.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33085", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191600.57+505940.5.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33081", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46042927.csv", "length": 281, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13620721.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33152", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223637.99+543144.7.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33166", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194000.06+313238.1.csv", "length": 128, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35230672.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33179", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221307.48+614923.6.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33199", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ002841.23+451600.2.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33202", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J184042.54-335053.6.csv", "length": 274, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29979041.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33220", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP49488464.csv", "length": 340, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33229", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56615209.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33246", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32803825.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33240", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J012720.60-300643.2.csv", "length": 294, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33267", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191854.01+014536.1.csv", "length": 95, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33266", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ010213.31+391701.3.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33276", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10798487.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29840529.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33300", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25242710.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33289", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17313879.csv", "length": 252, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19919370.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33307", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230510.55+631329.8.csv", "length": 315, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58939787.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33325", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J113433.30-702832.1.csv", "length": 370, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33288", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J080125.70-630010.3.csv", "length": 1318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33337", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10488202.csv", "length": 192, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33335", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J052434.80+685338.5.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33338", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29910844.csv", "length": 289, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33347", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195023.89+210706.6.csv", "length": 147, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33356", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP823966.csv", "length": 113, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33357", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J025730.45-174352.4.csv", "length": 278, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33363", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031118.20+491920.9.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33362", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J191014.74-541325.8.csv", "length": 463, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33367", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195944.19+435606.6.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33376", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13948813.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33393", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194646.62+301007.6.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33409", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5031669983878164992.csv", "length": 39, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190616.20+162739.8.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33425", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29965419.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33452", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10450569.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33443", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51808514.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ070710.84-041657.3.csv", "length": 74, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33465", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32676735.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33495", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201748.35+404845.4.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33498", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46367294.csv", "length": 497, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33532", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32658439.csv", "length": 77, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33541", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5722584188622683136.csv", "length": 46, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33524", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J081255.69-802625.9.csv", "length": 589, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33523", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J061601.78-592336.8.csv", "length": 750, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33568", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183955.67-040114.0.csv", "length": 97, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33561", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ011900.00+573244.4.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33562", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29916953.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33559", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221751.54+512606.1.csv", "length": 296, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33555", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58970962.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33606", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2199988356188744192.csv", "length": 42, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182500.64+563906.1.csv", "length": 245, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33626", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35401106.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33640", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ035225.18+684931.5.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33719", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ172406.10+341430.8.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220403.46+465701.1.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33707", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2964805036690596736.csv", "length": 88, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59586635.csv", "length": 225, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33728", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ005704.25+624846.6.csv", "length": 267, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33733", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ231003.82+621355.0.csv", "length": 318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33738", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35023462.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33741", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ042435.78+465442.0.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33753", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215406.71+485930.1.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194129.42+103025.2.csv", "length": 194, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29827526.csv", "length": 390, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33780", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202728.81+521319.3.csv", "length": 76, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33771", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29600121.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33775", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32704499.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33789", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201437.26+224015.8.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33785", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP27869192.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33828", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35337022.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33835", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182137.34+333747.3.csv", "length": 237, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33823", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J122423.41-475409.9.csv", "length": 986, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33833", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J035012.43+243157.8.csv", "length": 480, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33868", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031536.75+470158.1.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33860", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18922941.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33853", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19109858.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33878", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221206.31+523158.0.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ184555.26+135924.8.csv", "length": 206, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33907", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J154658.36-392241.5.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33908", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221517.78+523803.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33923", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J045632.37-665419.0.csv", "length": 705, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33952", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ003632.13+693632.2.csv", "length": 132, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33945", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ235847.98+664807.9.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29014177.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34029", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56504336.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34053", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59502011.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34058", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230532.47+632057.7.csv", "length": 313, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34095", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210024.63+385357.8.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34099", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51643508.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34097", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ025518.72+512510.6.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34109", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP923427.csv", "length": 182, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34115", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222714.26+443216.6.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34138", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195628.41+232832.0.csv", "length": 150, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194439.92+353805.8.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34142", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32703099.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34145", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13909677.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34176", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3054374999104019328.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34190", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000621.45+593232.5.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34196", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J094302.80-170716.0.csv", "length": 544, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34210", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29989035.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34241", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10996646.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34242", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ054950.63+153810.2.csv", "length": 94, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34283", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213631.02+450506.5.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34273", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP45879426.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34286", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5834615666118073984.csv", "length": 64, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34291", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21267615.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34308", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51949786.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35320191.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34324", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29934506.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19771350.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34950121.csv", "length": 275, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34332", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193728.57+090640.3.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34342", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28965561.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200548.92+303517.1.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34338", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56517288.csv", "length": 117, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34348", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J201703.44-371423.4.csv", "length": 192, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34359", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194135.77+162814.0.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34362", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10565677.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34381", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29480695.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34368", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ142813.33+514239.0.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34415", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ071508.05+005642.6.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34416", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195001.63+193221.5.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34419", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180032.88+400832.2.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ030508.48+470227.3.csv", "length": 56, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32880174.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34435", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP334785.csv", "length": 247, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35347538.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34482", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J141826.59+001428.6.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205158.49+443724.2.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34517", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28982007.csv", "length": 277, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34528", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10518747.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34527", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203423.30+082046.3.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34542", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP2072512.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34552", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225303.57+582906.2.csv", "length": 318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34566", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46739784.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211533.46+642412.8.csv", "length": 231, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34600", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193536.64+582503.5.csv", "length": 223, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35272611.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34641", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J173523.36-523331.5.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34644", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024500.98+561613.2.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28719614.csv", "length": 189, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34667", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6067498447929365760.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34661", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222818.47+562531.9.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34679", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32692369.csv", "length": 77, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28550039.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34680", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21252818.csv", "length": 266, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34696", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30258051.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40663128.csv", "length": 177, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34713", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201353.53+344842.0.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34714", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32990861.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34725", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190536.36+205854.5.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34724", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194935.03+423647.4.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29353656.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34787", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ153914.08-123316.9.csv", "length": 78, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34786", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212503.73+213011.1.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34798", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J113739.98-374218.9.csv", "length": 259, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34820", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192510.64+022411.8.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34842", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ232400.13+742505.1.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29975381.csv", "length": 169, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34845", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34968400.csv", "length": 272, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34859", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP6130656.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34856", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34942749.csv", "length": 275, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ020313.84+450353.7.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34896", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5752461222379143552.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59214915.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34903", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39215828.csv", "length": 379, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34954", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222810.74+522530.9.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34955", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10165261.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34957", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191429.09+694904.2.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34975", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J035439.26+343519.2.csv", "length": 137, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29468545.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34997", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ070513.85-122106.6.csv", "length": 66, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35000", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46837165.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35031", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191650.11+235121.9.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35060", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10370207.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35054", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32865775.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35059", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59463872.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35087", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4929368054776557184.csv", "length": 69, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35102", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29984053.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35114", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192547.33+090137.8.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35115", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ070345.47+353154.6.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35113", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35188105.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35140", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203452.74+503759.4.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35151", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31114645.csv", "length": 131, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222452.31+533319.2.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35176", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ234842.33+535122.8.csv", "length": 149, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35174", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ030936.14+531656.0.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35195", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ171507.41+153201.1.csv", "length": 69, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4490848595322325120.csv", "length": 41, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35206", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46832475.csv", "length": 497, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35213", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185915.45+054816.0.csv", "length": 255, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35228", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190030.27-060806.9.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35224", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ051346.56+433526.1.csv", "length": 113, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193849.86+264101.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35223", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30262883.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35253", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP37841262.csv", "length": 305, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35260", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32702189.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35268", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183054.10+080213.8.csv", "length": 182, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35269", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51736956.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35256519.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35281", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191920.16+301222.2.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35297", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055946.59+271852.0.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35299", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191122.96+361741.4.csv", "length": 81, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35294", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6071430743915130752.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35309", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10381847.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35314", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32686015.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J065015.37-053417.7.csv", "length": 189, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203614.90+462523.4.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203839.73+263040.8.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35357", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28672336.csv", "length": 93, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35350", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185633.77+323058.3.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35370", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ012153.36+320906.5.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59519509.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35403", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055700.72+125943.0.csv", "length": 137, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35400", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J170039.10-263658.2.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35406", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29005633.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35408", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19098045.csv", "length": 247, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203729.37+463511.0.csv", "length": 130, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35463", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ014956.38+535418.9.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35459", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210442.85+572129.6.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25555717.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35494", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4440521439859208320.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35479", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202244.69+385150.2.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35485", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024517.82+390700.4.csv", "length": 103, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35490", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28970867.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51909608.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35497", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34617705.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35505", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183205.68+153229.7.csv", "length": 128, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35498", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32922128.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35519", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28538214.csv", "length": 204, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4084046404209313920.csv", "length": 24, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35557", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51868349.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35558", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J161550.86+373221.3.csv", "length": 273, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35580", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59292293.csv", "length": 478, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19727572.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35599", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32907781.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ232551.09+562813.1.csv", "length": 306, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30935196.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35636", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193325.93+310121.3.csv", "length": 83, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53360939.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35630", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J152807.34+060802.1.csv", "length": 228, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35656", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212942.93+452632.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35671", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29964178.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35664", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203400.83+572009.1.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP50791569.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35675", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18827098.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35686", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13641251.csv", "length": 94, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35693", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220012.18+492204.0.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211537.75+100238.2.csv", "length": 72, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35714", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29816384.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17077698.csv", "length": 264, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35734", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35143581.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35732", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10969048.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35759", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221230.03+464703.5.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30609689.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J143907.80-001435.0.csv", "length": 434, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35783", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193222.20+174632.1.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35789", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ234024.47+562139.8.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35794", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210530.38+783756.0.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35781", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224219.56+583433.4.csv", "length": 333, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35800", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213114.57+463519.8.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35810", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195444.39+362821.6.csv", "length": 169, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35824", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195201.33+333337.1.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35804", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28485563.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51803472.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35832", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215717.77+542717.3.csv", "length": 336, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35835", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10386252.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35838", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30056955.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35854", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6035314299496494720.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35848", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J032405.68-084131.4.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35874", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30138793.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35872", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ170225.27+232141.9.csv", "length": 226, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190522.03+471207.4.csv", "length": 236, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35886", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31370099.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35898", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185912.19-001211.9.csv", "length": 92, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35915", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP50768400.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35905", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195924.27+282602.4.csv", "length": 155, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35903", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59229630.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35911", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32858510.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35921", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35262660.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35914", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J134407.48-001003.8.csv", "length": 226, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35926", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6709671233497390336.csv", "length": 35, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35940", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4795804707993358080.csv", "length": 43, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35929", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213128.60+491558.9.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35930", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190516.25+294130.7.csv", "length": 343, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35946", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59399382.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35964", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1191345695083956608.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35961", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29721778.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35966", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31016146.csv", "length": 222, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35960", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59180964.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222732.88+561236.5.csv", "length": 137, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35989", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211055.29+162029.1.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190841.13+245524.0.csv", "length": 72, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36020", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ072807.60-124028.4.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35999", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183421.29+295216.3.csv", "length": 503, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36022", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224244.91+531223.2.csv", "length": 243, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36033", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223130.31+514244.6.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36031", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200427.53+360832.9.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36044", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ064152.38-050222.0.csv", "length": 79, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36047", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201025.08+333627.2.csv", "length": 347, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36077", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055131.81+354607.1.csv", "length": 105, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36090", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191233.57+462533.9.csv", "length": 410, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36106", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1972891410655243904.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36105", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ054903.73+523458.0.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36119", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25291349.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36112", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191913.47+440654.1.csv", "length": 267, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36134", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28719843.csv", "length": 190, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36151", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID946933709156195456.csv", "length": 30, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36141", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31401797.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36146", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J081540.30-622107.6.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36164", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59320173.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36167", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10844686.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP33156911.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36175", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59286646.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36171", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP641466.csv", "length": 253, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36186", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51773591.csv", "length": 115, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36217", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID209012109560991360.csv", "length": 41, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36196", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190846.47+191754.4.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36229", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214248.61+480003.0.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36224", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ234353.90+513141.7.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30847096.csv", "length": 215, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36259", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200431.82+151933.3.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36262", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J225535.01-172718.2.csv", "length": 189, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36264", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30868182.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36238", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29610554.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36291", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51844721.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36287", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19015423.csv", "length": 284, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36306", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210918.30+174909.4.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25593066.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000018.70+612543.2.csv", "length": 252, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36319", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35050806.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36331", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ155716.06+695116.6.csv", "length": 351, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36350", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J044912.48-690940.4.csv", "length": 440, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36339", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J104448.70-363200.2.csv", "length": 651, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36356", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53907883.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36359", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35326964.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36371", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5880389194131434368.csv", "length": 44, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36361", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35073542.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31268612.csv", "length": 237, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36375", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59200313.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36413", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2698195182306771328.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29068061.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36419", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51768323.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24853126.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36429", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ174325.24+640021.7.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36441", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ043215.89+445333.3.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36431", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ165109.64+304115.9.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36445", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194053.80+183944.3.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36458", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25404785.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36470", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35172381.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36481", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13755242.csv", "length": 138, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51800159.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36488", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31399587.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36500", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222608.75+570948.1.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36503", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192736.40+290753.5.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36507", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J093856.65-375820.2.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36515", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29669383.csv", "length": 264, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36523", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J081142.60-155900.2.csv", "length": 265, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36538", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ060715.23+643321.4.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36529", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19120047.csv", "length": 270, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36548", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35096700.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36552", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10274228.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36550", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32645404.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36564", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24811386.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36567", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59512778.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36577", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP27870271.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32903254.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36616", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59186550.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36656", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ044412.96+415915.7.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36668", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ184232.79+294532.9.csv", "length": 270, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46683993.csv", "length": 235, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36686", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031206.29+584036.8.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36697", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2038327024125788928.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36688", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J040907.50+213102.8.csv", "length": 244, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34618314.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36690", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19032751.csv", "length": 341, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36694", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ064914.53+035927.1.csv", "length": 87, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36732", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ181201.19+702910.3.csv", "length": 301, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36738", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17615773.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36749", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34903138.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36767", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ050849.93+424248.7.csv", "length": 111, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36771", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194244.47+194141.4.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36786", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192459.79+431557.9.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36784", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28956681.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36796", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59256838.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36813", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215639.75+610446.2.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214107.69+374208.8.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36843", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195336.16+532353.6.csv", "length": 78, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36824", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J163403.49+452455.9.csv", "length": 565, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36846", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29939462.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36886", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2877977187558268544.csv", "length": 52, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36887", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203544.69+145348.9.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29371725.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36901", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J002030.73+421309.5.csv", "length": 223, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36909", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28689687.csv", "length": 180, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36942", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28792058.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36969", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5930109213066340480.csv", "length": 45, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36951", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32695665.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36960", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192158.15+244544.2.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36976", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35384114.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36975", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29366020.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36985", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10021132.csv", "length": 357, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37011", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ181010.61+392311.9.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37008", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221635.17+235834.0.csv", "length": 70, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37019", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10584002.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37034", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29748068.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37060", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59501162.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9769518.csv", "length": 255, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37049", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ174909.58+274339.7.csv", "length": 299, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37066", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP787605.csv", "length": 225, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37068", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28758172.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37087", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3033023514088080896.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37094", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192809.93+102027.8.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37097", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192616.01+282748.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37106", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29601958.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37112", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP45681378.csv", "length": 236, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37113", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46653465.csv", "length": 269, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37117", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213832.52+281241.0.csv", "length": 101, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37116", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ162524.76+425948.6.csv", "length": 276, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37119", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J004554.97-205842.6.csv", "length": 535, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31357479.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37134", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5590000957676707840.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37130", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25722298.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37132", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221817.94+524407.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37135", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP12102535.csv", "length": 292, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37145", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39732811.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37154", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5241086473236332288.csv", "length": 48, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37157", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224339.90+530931.4.csv", "length": 319, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37177", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J070621.39+114641.6.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37188", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205508.40+531441.5.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37194", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195044.71+222239.9.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37185", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221344.98+445900.5.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37191", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ021440.52+532716.6.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP11322196.csv", "length": 263, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015036.82+551509.1.csv", "length": 124, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37200", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29759363.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37247", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5955011016768267392.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37243", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20136109.csv", "length": 275, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37265", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29017803.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37263", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221221.08+561610.1.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37280", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24815520.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195532.70+394505.6.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37290", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J071928.29+083449.9.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37308", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10351142.csv", "length": 369, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37301", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40475556.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37305", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ023638.61+621053.5.csv", "length": 240, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194652.16+105501.1.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37312", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58691477.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37338", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J145344.01-705153.9.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37344", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29876300.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37353", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ204219.24+382753.7.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37351", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34913205.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37370", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4530970564901775616.csv", "length": 83, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37365", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J144455.72+004745.0.csv", "length": 197, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37361", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214911.09+561101.4.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP57912090.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37397", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212527.02+582903.0.csv", "length": 324, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37391", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205253.45+465226.9.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37454", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180325.71+190332.7.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37467", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46577906.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37504", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183621.03-123410.0.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37500", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51757540.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28878327.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37524", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24882359.csv", "length": 347, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10605547.csv", "length": 412, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37564", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP6152441.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37565", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32937101.csv", "length": 183, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37574", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194450.41+291602.3.csv", "length": 134, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37582", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5101087169464377856.csv", "length": 65, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37589", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000233.75+604454.5.csv", "length": 298, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37597", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ064142.86+242236.1.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37604", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25159544.csv", "length": 174, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37637", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59464217.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37644", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31897149.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP37620882.csv", "length": 305, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37645", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32921249.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37653", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24835599.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37672", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29895040.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37659", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J075953.64-104547.1.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37668", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ143607.83+343455.2.csv", "length": 367, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37691", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ111332.16+661226.0.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37681", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29790903.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37707", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20523501.csv", "length": 247, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37709", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ044628.77+441529.9.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37720", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10467470.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37738", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29545335.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37749", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222244.82+451228.6.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37755", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9534000.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37760", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210551.86+125334.5.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37752", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180326.28+333659.1.csv", "length": 481, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224406.89+553430.9.csv", "length": 324, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37767", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225052.90+544215.8.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37784", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195317.20+595941.1.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37782", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195033.04+332948.0.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37822", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59485130.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37846", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32636830.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37835", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220710.27+472634.6.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37858", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ233238.97+693332.1.csv", "length": 63, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37853", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32920511.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51738066.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37864", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J065847.25+062036.9.csv", "length": 177, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37884", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ032628.44+624931.1.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37913", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35293900.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37912", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215641.25+441907.2.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ171043.78+392836.0.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37938", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31929840.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37955", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32808336.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37987", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ173817.30+231145.9.csv", "length": 223, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38009", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ014600.76+584550.5.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38028", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51664226.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194910.37+111400.7.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37998", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J091801.09-073744.2.csv", "length": 1109, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38039", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185743.71+130800.6.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38034", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193826.38+151206.3.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38046", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18899208.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38062", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5983396425582947968.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38071", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194440.12+293159.0.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J182352.81-303446.9.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38080", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190631.01-002706.5.csv", "length": 88, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38105", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30018068.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38115", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193110.81+292851.5.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195550.32+210852.4.csv", "length": 149, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24895213.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38202", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J052333.56+444536.3.csv", "length": 226, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38219", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51694049.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38227", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055346.54+293856.1.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38246", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6903246307173663488.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38248", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51694050.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32990694.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38298", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193419.48+125218.9.csv", "length": 133, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29899407.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38312", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32702774.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38326", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21451850.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38332", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ004652.91+502011.8.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38342", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30042567.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38350", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51732435.csv", "length": 124, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38355", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195207.76+313804.8.csv", "length": 172, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38361", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J063837.13-174301.5.csv", "length": 172, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38362", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39794351.csv", "length": 367, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38374", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224423.93+683502.5.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38380", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221558.48+543503.5.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38379", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223248.41+550444.9.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38407", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215157.51+495100.3.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38398", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30085907.csv", "length": 207, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38403", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18822544.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38424", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59472703.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38434", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195453.04+372740.8.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38432", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220630.16+630727.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38448", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ065435.64+001452.1.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38452", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200055.13+171838.9.csv", "length": 64, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40475566.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38478", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ161940.21+025157.2.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38486", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ022315.78+354010.4.csv", "length": 91, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38505", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230728.30+590944.2.csv", "length": 312, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59421438.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38539", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40564789.csv", "length": 384, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10383290.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38547", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J050553.31-680707.6.csv", "length": 440, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38571", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J053530.16-061206.0.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38557", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30609692.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38569", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192305.55+223015.6.csv", "length": 331, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38582", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP45424180.csv", "length": 228, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38591", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195324.53+213640.1.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38590", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ152308.63+475752.2.csv", "length": 269, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38609", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211946.85+543129.8.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38617", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211108.48+130352.8.csv", "length": 72, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38618", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29983243.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29384223.csv", "length": 101, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38610", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP2135598.csv", "length": 450, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38632", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24826264.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5701891452804411520.csv", "length": 46, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38658", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024212.05+573438.1.csv", "length": 117, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38654", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35272695.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38652", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30005575.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38669", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J053802.61-020444.5.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59593296.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38684", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ141935.11+174148.0.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38698", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ232849.07+550638.4.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38697", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51652825.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38702", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190432.99+531727.9.csv", "length": 103, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38695", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10385371.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38713", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000314.44+563514.0.csv", "length": 196, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38714", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J190602.22-631012.6.csv", "length": 371, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38719", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32921293.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ003407.01+622946.6.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38775", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J000839.60-760721.0.csv", "length": 487, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38790", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10523324.csv", "length": 190, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38827", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10896733.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5593804065317919744.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38851", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5249750934083165312.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38879", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10439742.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38875", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ235852.75+644207.1.csv", "length": 278, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38881", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191649.10+274945.6.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38888", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46985884.csv", "length": 285, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38908", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34716200.csv", "length": 150, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38909", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J071701.15-135729.6.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38918", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34979593.csv", "length": 259, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38892", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51631445.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38921", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58769338.csv", "length": 473, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J221525.99+253155.9.csv", "length": 419, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38951", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J100713.37-662419.1.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38953", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35003966.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38969", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ030040.50+575527.9.csv", "length": 220, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39023", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29892993.csv", "length": 214, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39032", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59296461.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39044", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191723.84+092204.5.csv", "length": 138, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39048", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230839.22+531707.1.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39055", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29840734.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39083", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193733.10+341921.6.csv", "length": 258, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39103", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5539084292182237952.csv", "length": 51, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39095", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28578452.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39120", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J182541.03-265435.6.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39118", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205756.93+220132.3.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39123", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35093212.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39113", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ160950.63+462844.1.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39139", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32853553.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39161", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19915175.csv", "length": 281, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39168", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J032152.59+440408.3.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39180", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29700671.csv", "length": 271, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39181", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29525151.csv", "length": 441, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39194", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213517.82+462928.1.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39192", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP16575042.csv", "length": 133, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39220", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195352.37+174658.0.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39222", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192648.63+100019.0.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29514721.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39216", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35219105.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222919.41+602200.8.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39259", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ051246.21+365817.9.csv", "length": 204, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182740.87+144230.6.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39278", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP44544044.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39284", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59518244.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39303", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10563865.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39304", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J115827.66-424328.8.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39333", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35057899.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39355", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ035822.47+592807.6.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39352", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225824.15+554612.1.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39357", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53363062.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39354", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39236864.csv", "length": 203, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39358", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230627.01+564217.7.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39370", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182731.76+163032.7.csv", "length": 65, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39353", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29601628.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39383", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ184439.85+331000.3.csv", "length": 194, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39396", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201436.04+344243.8.csv", "length": 169, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221901.82+604646.0.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213846.37+481922.2.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39424", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J065032.37+000025.5.csv", "length": 432, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39448", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212950.17+121026.5.csv", "length": 74, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39437", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185655.14+365234.2.csv", "length": 277, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39445", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9545449.csv", "length": 201, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39442", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9536971.csv", "length": 207, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39447", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31032706.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39450", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32886227.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39461", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200812.46+324330.7.csv", "length": 348, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192638.09+293122.1.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39480", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192549.45+044756.5.csv", "length": 81, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39494", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP48333915.csv", "length": 180, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39521", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211345.37+460144.2.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39534", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191943.66+425735.0.csv", "length": 82, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39548", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP33174590.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35191283.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185559.90+323054.5.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39591", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35008443.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39581", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29781077.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39589", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35020607.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39588", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J211506.36+435005.7.csv", "length": 91, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39606", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215309.50+685209.9.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39603", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46759660.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39612", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56828450.csv", "length": 260, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39614", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51588668.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39628", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195907.29+212807.8.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180424.69+021757.7.csv", "length": 106, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39631", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30015932.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210005.99+414221.7.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39666", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185245.80+073302.6.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39664", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192200.13+052330.9.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29842478.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59180968.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39712", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP33011230.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055606.04+441412.0.csv", "length": 116, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J042901.42+323532.9.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39711", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35054962.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39743", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201256.97+464239.9.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210800.45+364501.0.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39780", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ125918.74+334025.6.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39792", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225949.07+584128.0.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39821", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194114.92+163702.8.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39824", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10951496.csv", "length": 98, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19170176.csv", "length": 302, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21454307.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39844", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP385257.csv", "length": 200, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214143.26+482255.9.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39840", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10913930.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29412387.csv", "length": 439, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39866", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191334.80+333604.2.csv", "length": 82, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39854", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59157725.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39877", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56922457.csv", "length": 206, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3061049343922478208.csv", "length": 38, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39892", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ040133.84+521425.7.csv", "length": 114, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39896", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29635634.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39911", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35039954.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39913", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30025029.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39910", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31412823.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39920", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31815881.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39939", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32935533.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39928", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32694704.csv", "length": 370, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39953", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13678225.csv", "length": 109, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_511_3_15457", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_511_3_15457.npy", "length": 15457, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$511,:3,:15457"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_10_3_15496", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_10_3_15496.npy", "length": 15496, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket1$10,:3,:15496"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_549_3_14000", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_549_3_14000.npy", "length": 14000, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$549,:3,:14000"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_457_3_17054", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_457_3_17054.npy", "length": 17054, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$457,:3,:17054"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_25_3_15094", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_25_3_15094.npy", "length": 15094, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$25,:3,:15094"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_727_3_15242", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_727_3_15242.npy", "length": 15242, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$727,:3,:15242"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_138_3_15582", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_138_3_15582.npy", "length": 15582, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$138,:3,:15582"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_664_3_15320", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_664_3_15320.npy", "length": 15320, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$664,:3,:15320"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_344_3_15232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_344_3_15232.npy", "length": 15232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$344,:3,:15232"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_483_3_15307", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_483_3_15307.npy", "length": 15307, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$483,:3,:15307"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_541_3_15386", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_541_3_15386.npy", "length": 15386, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$541,:3,:15386"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_997_3_15063", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_997_3_15063.npy", "length": 15063, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$997,:3,:15063"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_623_3_15831", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_623_3_15831.npy", "length": 15831, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$623,:3,:15831"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_834_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_834_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$834,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_1018_3_15977", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_1018_3_15977.npy", "length": 15977, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$1018,:3,:15977"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_139_3_15311", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_139_3_15311.npy", "length": 15311, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$139,:3,:15311"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_707_3_15569", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_707_3_15569.npy", "length": 15569, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$707,:3,:15569"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_923_3_15264", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_923_3_15264.npy", "length": 15264, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$923,:3,:15264"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_822_3_16493", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_822_3_16493.npy", "length": 16493, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$822,:3,:16493"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_977_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_977_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$977,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_846_3_14945", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_846_3_14945.npy", "length": 14945, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$846,:3,:14945"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_298_3_15369", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_298_3_15369.npy", "length": 15369, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$298,:3,:15369"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_152_3_15414", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_152_3_15414.npy", "length": 15414, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$152,:3,:15414"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_135_3_15142", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_135_3_15142.npy", "length": 15142, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$135,:3,:15142"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_497_3_14878", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_497_3_14878.npy", "length": 14878, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$497,:3,:14878"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_787_3_14589", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_787_3_14589.npy", "length": 14589, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$787,:3,:14589"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_822_3_16357", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_822_3_16357.npy", "length": 16357, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$822,:3,:16357"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_632_3_15752", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_632_3_15752.npy", "length": 15752, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$632,:3,:15752"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_22_3_16418", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_22_3_16418.npy", "length": 16418, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$22,:3,:16418"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_651_3_14710", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_651_3_14710.npy", "length": 14710, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$651,:3,:14710"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_625_3_15323", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_625_3_15323.npy", "length": 15323, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$625,:3,:15323"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_822_3_14691", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_822_3_14691.npy", "length": 14691, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$822,:3,:14691"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_928_3_15515", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_928_3_15515.npy", "length": 15515, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$928,:3,:15515"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_142_3_15753", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_142_3_15753.npy", "length": 15753, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$142,:3,:15753"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_769_3_14837", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_769_3_14837.npy", "length": 14837, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$769,:3,:14837"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_99_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_99_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$99,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_862_3_15598", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_862_3_15598.npy", "length": 15598, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$862,:3,:15598"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_932_3_15660", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_932_3_15660.npy", "length": 15660, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$932,:3,:15660"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_790_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_790_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$790,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_366_3_16587", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_366_3_16587.npy", "length": 16587, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$366,:3,:16587"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_29_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_29_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$29,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_363_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_363_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$363,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_992_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_992_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$992,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_802_3_14980", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_802_3_14980.npy", "length": 14980, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$802,:3,:14980"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_377_3_15229", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_377_3_15229.npy", "length": 15229, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$377,:3,:15229"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_772_3_15856", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_772_3_15856.npy", "length": 15856, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$772,:3,:15856"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_669_3_15174", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_669_3_15174.npy", "length": 15174, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$669,:3,:15174"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_137_3_15389", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_137_3_15389.npy", "length": 15389, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$137,:3,:15389"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_433_3_15716", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_433_3_15716.npy", "length": 15716, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$433,:3,:15716"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_412_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_412_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$412,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_768_3_14741", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_768_3_14741.npy", "length": 14741, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$768,:3,:14741"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_38_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_38_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$38,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_461_3_15013", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_461_3_15013.npy", "length": 15013, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$461,:3,:15013"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_208_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_208_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$208,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_549_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_549_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$549,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_215_3_15384", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_215_3_15384.npy", "length": 15384, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$215,:3,:15384"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_763_3_17324", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_763_3_17324.npy", "length": 17324, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$763,:3,:17324"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_638_3_14820", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_638_3_14820.npy", "length": 14820, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$638,:3,:14820"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_379_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_379_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$379,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_420_3_15252", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_420_3_15252.npy", "length": 15252, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$420,:3,:15252"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_310_3_17488", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_310_3_17488.npy", "length": 17488, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$310,:3,:17488"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_287_3_16112", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_287_3_16112.npy", "length": 16112, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$287,:3,:16112"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_250_3_15311", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_250_3_15311.npy", "length": 15311, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$250,:3,:15311"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_555_3_16114", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_555_3_16114.npy", "length": 16114, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$555,:3,:16114"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_494_3_14881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_494_3_14881.npy", "length": 14881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$494,:3,:14881"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_17_3_15304", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_17_3_15304.npy", "length": 15304, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$17,:3,:15304"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_707_3_15822", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_707_3_15822.npy", "length": 15822, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$707,:3,:15822"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_86_3_15700", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_86_3_15700.npy", "length": 15700, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$86,:3,:15700"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_909_3_16253", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_909_3_16253.npy", "length": 16253, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$909,:3,:16253"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_96_3_15354", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_96_3_15354.npy", "length": 15354, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$96,:3,:15354"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_803_3_15940", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_803_3_15940.npy", "length": 15940, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$803,:3,:15940"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_328_3_16000", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_328_3_16000.npy", "length": 16000, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$328,:3,:16000"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_792_3_15413", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_792_3_15413.npy", "length": 15413, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$792,:3,:15413"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_732_3_15198", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_732_3_15198.npy", "length": 15198, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$732,:3,:15198"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_162_3_16314", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_162_3_16314.npy", "length": 16314, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$162,:3,:16314"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_261_3_15618", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_261_3_15618.npy", "length": 15618, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$261,:3,:15618"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_8_3_16035", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_8_3_16035.npy", "length": 16035, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$8,:3,:16035"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_612_3_15738", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_612_3_15738.npy", "length": 15738, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$612,:3,:15738"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_467_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_467_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$467,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_204_3_15854", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_204_3_15854.npy", "length": 15854, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$204,:3,:15854"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_853_3_15907", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_853_3_15907.npy", "length": 15907, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$853,:3,:15907"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_118_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_118_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$118,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_997_3_16567", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_997_3_16567.npy", "length": 16567, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$997,:3,:16567"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_935_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_935_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$935,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_553_3_16060", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_553_3_16060.npy", "length": 16060, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$553,:3,:16060"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_828_3_15092", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_828_3_15092.npy", "length": 15092, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$828,:3,:15092"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_969_3_15791", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_969_3_15791.npy", "length": 15791, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$969,:3,:15791"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_362_3_16433", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_362_3_16433.npy", "length": 16433, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$362,:3,:16433"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_322_3_15052", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_322_3_15052.npy", "length": 15052, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$322,:3,:15052"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_441_3_15369", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_441_3_15369.npy", "length": 15369, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$441,:3,:15369"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_79_3_15742", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_79_3_15742.npy", "length": 15742, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$79,:3,:15742"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_344_3_15858", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_344_3_15858.npy", "length": 15858, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$344,:3,:15858"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_914_3_15645", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_914_3_15645.npy", "length": 15645, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$914,:3,:15645"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_444_3_15603", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_444_3_15603.npy", "length": 15603, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$444,:3,:15603"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_27_3_15280", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_27_3_15280.npy", "length": 15280, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$27,:3,:15280"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_879_3_16298", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_879_3_16298.npy", "length": 16298, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$879,:3,:16298"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_982_3_16433", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_982_3_16433.npy", "length": 16433, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$982,:3,:16433"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_622_3_15148", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_622_3_15148.npy", "length": 15148, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$622,:3,:15148"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_476_3_14795", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_476_3_14795.npy", "length": 14795, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$476,:3,:14795"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_240_3_15555", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_240_3_15555.npy", "length": 15555, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$240,:3,:15555"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_197_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_197_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$197,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_827_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_827_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$827,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_562_3_15469", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_562_3_15469.npy", "length": 15469, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$562,:3,:15469"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_859_3_16339", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_859_3_16339.npy", "length": 16339, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$859,:3,:16339"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_97_3_16891", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_97_3_16891.npy", "length": 16891, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$97,:3,:16891"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_58_3_14804", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_58_3_14804.npy", "length": 14804, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$58,:3,:14804"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_990_3_15445", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_990_3_15445.npy", "length": 15445, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket1$990,:3,:15445"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_140_3_15130", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_140_3_15130.npy", "length": 15130, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$140,:3,:15130"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_120_3_16150", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_120_3_16150.npy", "length": 16150, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$120,:3,:16150"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_396_3_15967", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_396_3_15967.npy", "length": 15967, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$396,:3,:15967"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_182_3_14520", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_182_3_14520.npy", "length": 14520, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$182,:3,:14520"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_637_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_637_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$637,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_519_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_519_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$519,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_917_3_15189", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_917_3_15189.npy", "length": 15189, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$917,:3,:15189"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_933_3_15092", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_933_3_15092.npy", "length": 15092, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$933,:3,:15092"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_530_3_14795", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_530_3_14795.npy", "length": 14795, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$530,:3,:14795"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_175_3_14897", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_175_3_14897.npy", "length": 14897, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$175,:3,:14897"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_264_3_15059", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_264_3_15059.npy", "length": 15059, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$264,:3,:15059"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_936_3_14895", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_936_3_14895.npy", "length": 14895, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$936,:3,:14895"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_469_3_15408", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_469_3_15408.npy", "length": 15408, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$469,:3,:15408"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_340_3_16098", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_340_3_16098.npy", "length": 16098, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$340,:3,:16098"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_1012_3_16232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_1012_3_16232.npy", "length": 16232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$1012,:3,:16232"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_120_3_15147", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_120_3_15147.npy", "length": 15147, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$120,:3,:15147"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_948_3_16660", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_948_3_16660.npy", "length": 16660, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$948,:3,:16660"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_1_3_15456", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_1_3_15456.npy", "length": 15456, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$1,:3,:15456"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_799_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_799_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$799,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_145_3_15947", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_145_3_15947.npy", "length": 15947, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$145,:3,:15947"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_188_3_15798", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_188_3_15798.npy", "length": 15798, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$188,:3,:15798"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_855_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_855_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$855,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_726_3_15022", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_726_3_15022.npy", "length": 15022, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$726,:3,:15022"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_209_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_209_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$209,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_69_3_15022", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_69_3_15022.npy", "length": 15022, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$69,:3,:15022"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_189_3_15169", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_189_3_15169.npy", "length": 15169, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$189,:3,:15169"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_669_3_14948", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_669_3_14948.npy", "length": 14948, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$669,:3,:14948"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_796_3_14964", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_796_3_14964.npy", "length": 14964, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$796,:3,:14964"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_811_3_15766", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_811_3_15766.npy", "length": 15766, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$811,:3,:15766"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_938_3_16273", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_938_3_16273.npy", "length": 16273, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$938,:3,:16273"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_683_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_683_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$683,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_232_3_15945", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_232_3_15945.npy", "length": 15945, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$232,:3,:15945"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_860_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_860_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$860,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_811_3_16396", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_811_3_16396.npy", "length": 16396, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$811,:3,:16396"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_1013_3_14953", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_1013_3_14953.npy", "length": 14953, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$1013,:3,:14953"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_655_3_14814", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_655_3_14814.npy", "length": 14814, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$655,:3,:14814"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_366_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_366_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$366,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_394_3_16160", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_394_3_16160.npy", "length": 16160, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$394,:3,:16160"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_276_3_14812", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_276_3_14812.npy", "length": 14812, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$276,:3,:14812"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_703_3_16248", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_703_3_16248.npy", "length": 16248, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$703,:3,:16248"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_942_3_16024", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_942_3_16024.npy", "length": 16024, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$942,:3,:16024"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_872_3_16265", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_872_3_16265.npy", "length": 16265, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$872,:3,:16265"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_16_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_16_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$16,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_465_3_14587", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_465_3_14587.npy", "length": 14587, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$465,:3,:14587"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_1013_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_1013_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$1013,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_600_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_600_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$600,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_679_3_15516", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_679_3_15516.npy", "length": 15516, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$679,:3,:15516"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_72_3_15069", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_72_3_15069.npy", "length": 15069, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$72,:3,:15069"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_926_3_16417", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_926_3_16417.npy", "length": 16417, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$926,:3,:16417"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_925_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_925_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$925,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_728_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_728_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$728,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_464_3_16339", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_464_3_16339.npy", "length": 16339, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$464,:3,:16339"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_869_3_14881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_869_3_14881.npy", "length": 14881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$869,:3,:14881"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_589_3_15686", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_589_3_15686.npy", "length": 15686, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$589,:3,:15686"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_782_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_782_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$782,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_644_3_16808", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_644_3_16808.npy", "length": 16808, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$644,:3,:16808"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_892_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_892_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$892,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_78_3_15332", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_78_3_15332.npy", "length": 15332, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket4$78,:3,:15332"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_620_3_15689", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_620_3_15689.npy", "length": 15689, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$620,:3,:15689"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_719_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_719_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$719,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_446_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_446_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$446,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_680_3_15507", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_680_3_15507.npy", "length": 15507, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$680,:3,:15507"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_687_3_16416", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_687_3_16416.npy", "length": 16416, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$687,:3,:16416"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_523_3_16597", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_523_3_16597.npy", "length": 16597, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$523,:3,:16597"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_739_3_16494", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_739_3_16494.npy", "length": 16494, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$739,:3,:16494"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_441_3_15450", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_441_3_15450.npy", "length": 15450, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$441,:3,:15450"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_1015_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_1015_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$1015,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_879_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_879_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$879,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_881_3_16232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_881_3_16232.npy", "length": 16232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$881,:3,:16232"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_936_3_15257", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_936_3_15257.npy", "length": 15257, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$936,:3,:15257"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_601_3_15978", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_601_3_15978.npy", "length": 15978, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$601,:3,:15978"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_413_3_15519", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_413_3_15519.npy", "length": 15519, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$413,:3,:15519"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_535_3_15286", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_535_3_15286.npy", "length": 15286, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$535,:3,:15286"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_429_3_15800", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_429_3_15800.npy", "length": 15800, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$429,:3,:15800"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_929_3_15898", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_929_3_15898.npy", "length": 15898, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$929,:3,:15898"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_229_3_14844", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_229_3_14844.npy", "length": 14844, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$229,:3,:14844"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_890_3_15823", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_890_3_15823.npy", "length": 15823, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$890,:3,:15823"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_68_3_15831", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_68_3_15831.npy", "length": 15831, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$68,:3,:15831"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_386_3_15122", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_386_3_15122.npy", "length": 15122, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$386,:3,:15122"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_263_3_16716", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_263_3_16716.npy", "length": 16716, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$263,:3,:16716"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_411_3_16220", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_411_3_16220.npy", "length": 16220, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$411,:3,:16220"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_398_3_15167", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_398_3_15167.npy", "length": 15167, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$398,:3,:15167"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_142_3_17117", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_142_3_17117.npy", "length": 17117, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$142,:3,:17117"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_785_3_15130", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_785_3_15130.npy", "length": 15130, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$785,:3,:15130"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_238_3_15691", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_238_3_15691.npy", "length": 15691, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$238,:3,:15691"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_890_3_16407", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_890_3_16407.npy", "length": 16407, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$890,:3,:16407"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_816_3_15084", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_816_3_15084.npy", "length": 15084, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$816,:3,:15084"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_1011_3_16737", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_1011_3_16737.npy", "length": 16737, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$1011,:3,:16737"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_680_3_14427", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_680_3_14427.npy", "length": 14427, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$680,:3,:14427"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_490_3_17471", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_490_3_17471.npy", "length": 17471, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$490,:3,:17471"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_547_3_14613", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_547_3_14613.npy", "length": 14613, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$547,:3,:14613"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_779_3_15470", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_779_3_15470.npy", "length": 15470, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$779,:3,:15470"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_98_3_14900", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_98_3_14900.npy", "length": 14900, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$98,:3,:14900"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_192_3_14799", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_192_3_14799.npy", "length": 14799, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$192,:3,:14799"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_368_3_15775", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_368_3_15775.npy", "length": 15775, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$368,:3,:15775"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_431_3_14880", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_431_3_14880.npy", "length": 14880, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$431,:3,:14880"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_80_3_15031", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_80_3_15031.npy", "length": 15031, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket4$80,:3,:15031"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_406_3_15986", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_406_3_15986.npy", "length": 15986, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$406,:3,:15986"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_61_3_15695", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_61_3_15695.npy", "length": 15695, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$61,:3,:15695"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_16_3_15586", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_16_3_15586.npy", "length": 15586, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$16,:3,:15586"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_441_3_14642", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_441_3_14642.npy", "length": 14642, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$441,:3,:14642"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_939_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_939_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$939,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_72_3_15937", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_72_3_15937.npy", "length": 15937, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$72,:3,:15937"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_253_3_15531", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_253_3_15531.npy", "length": 15531, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$253,:3,:15531"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_1008_3_16213", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_1008_3_16213.npy", "length": 16213, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$1008,:3,:16213"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_413_3_16681", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_413_3_16681.npy", "length": 16681, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$413,:3,:16681"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_578_3_15348", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_578_3_15348.npy", "length": 15348, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$578,:3,:15348"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_325_3_14767", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_325_3_14767.npy", "length": 14767, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$325,:3,:14767"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_330_3_14789", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_330_3_14789.npy", "length": 14789, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$330,:3,:14789"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_83_3_15407", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_83_3_15407.npy", "length": 15407, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$83,:3,:15407"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_49_3_15379", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_49_3_15379.npy", "length": 15379, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$49,:3,:15379"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_196_3_15007", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_196_3_15007.npy", "length": 15007, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$196,:3,:15007"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_898_3_16182", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_898_3_16182.npy", "length": 16182, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$898,:3,:16182"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_147_3_16699", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_147_3_16699.npy", "length": 16699, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$147,:3,:16699"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_345_3_15730", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_345_3_15730.npy", "length": 15730, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$345,:3,:15730"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_131_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_131_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$131,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_661_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_661_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$661,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_164_3_14776", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_164_3_14776.npy", "length": 14776, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$164,:3,:14776"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_664_3_16574", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_664_3_16574.npy", "length": 16574, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$664,:3,:16574"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_659_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_659_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$659,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_351_3_14988", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_351_3_14988.npy", "length": 14988, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$351,:3,:14988"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_306_3_15477", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_306_3_15477.npy", "length": 15477, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$306,:3,:15477"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_986_3_15625", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_986_3_15625.npy", "length": 15625, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$986,:3,:15625"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_122_3_15282", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_122_3_15282.npy", "length": 15282, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$122,:3,:15282"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_223_3_15570", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_223_3_15570.npy", "length": 15570, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$223,:3,:15570"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_955_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_955_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$955,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_295_3_15795", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_295_3_15795.npy", "length": 15795, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$295,:3,:15795"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_662_3_15854", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_662_3_15854.npy", "length": 15854, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$662,:3,:15854"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_317_3_15338", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_317_3_15338.npy", "length": 15338, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$317,:3,:15338"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_157_3_15267", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_157_3_15267.npy", "length": 15267, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$157,:3,:15267"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_550_3_15177", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_550_3_15177.npy", "length": 15177, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$550,:3,:15177"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_201_3_15951", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_201_3_15951.npy", "length": 15951, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$201,:3,:15951"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_992_3_14944", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_992_3_14944.npy", "length": 14944, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$992,:3,:14944"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_794_3_16342", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_794_3_16342.npy", "length": 16342, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$794,:3,:16342"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_783_3_16256", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_783_3_16256.npy", "length": 16256, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$783,:3,:16256"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_514_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_514_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$514,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_371_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_371_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$371,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_493_3_15427", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_493_3_15427.npy", "length": 15427, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$493,:3,:15427"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_605_3_15633", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_605_3_15633.npy", "length": 15633, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$605,:3,:15633"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_896_3_15404", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_896_3_15404.npy", "length": 15404, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$896,:3,:15404"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_274_3_15126", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_274_3_15126.npy", "length": 15126, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$274,:3,:15126"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_647_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_647_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$647,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_886_3_15007", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_886_3_15007.npy", "length": 15007, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$886,:3,:15007"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_685_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_685_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$685,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_91_3_15014", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_91_3_15014.npy", "length": 15014, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$91,:3,:15014"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_153_3_16279", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_153_3_16279.npy", "length": 16279, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$153,:3,:16279"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_166_3_15488", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_166_3_15488.npy", "length": 15488, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$166,:3,:15488"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_867_3_15329", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_867_3_15329.npy", "length": 15329, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$867,:3,:15329"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_1023_3_15979", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_1023_3_15979.npy", "length": 15979, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$1023,:3,:15979"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_514_3_14870", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_514_3_14870.npy", "length": 14870, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$514,:3,:14870"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_187_3_15652", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_187_3_15652.npy", "length": 15652, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$187,:3,:15652"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_815_3_17040", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_815_3_17040.npy", "length": 17040, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$815,:3,:17040"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_955_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_955_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$955,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_839_3_15826", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_839_3_15826.npy", "length": 15826, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$839,:3,:15826"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_512_3_14743", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_512_3_14743.npy", "length": 14743, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$512,:3,:14743"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_932_3_15782", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_932_3_15782.npy", "length": 15782, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$932,:3,:15782"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_534_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_534_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$534,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_633_3_16327", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_633_3_16327.npy", "length": 16327, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$633,:3,:16327"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_931_3_15151", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_931_3_15151.npy", "length": 15151, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$931,:3,:15151"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_752_3_16089", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_752_3_16089.npy", "length": 16089, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$752,:3,:16089"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_91_3_15594", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_91_3_15594.npy", "length": 15594, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket3$91,:3,:15594"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_814_3_14976", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_814_3_14976.npy", "length": 14976, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$814,:3,:14976"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_29_3_15881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_29_3_15881.npy", "length": 15881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$29,:3,:15881"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_384_3_14000", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_384_3_14000.npy", "length": 14000, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$384,:3,:14000"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_497_3_16922", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_497_3_16922.npy", "length": 16922, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$497,:3,:16922"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_673_3_15569", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_673_3_15569.npy", "length": 15569, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$673,:3,:15569"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_735_3_15230", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_735_3_15230.npy", "length": 15230, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$735,:3,:15230"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "13_58_3_14712", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/13_58_3_14712.npy", "length": 14712, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket13$58,:3,:14712"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_48_3_15163", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_48_3_15163.npy", "length": 15163, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$48,:3,:15163"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_167_3_15060", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_167_3_15060.npy", "length": 15060, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$167,:3,:15060"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_962_3_14848", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_962_3_14848.npy", "length": 14848, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$962,:3,:14848"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_546_3_15817", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_546_3_15817.npy", "length": 15817, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$546,:3,:15817"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_235_3_14726", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_235_3_14726.npy", "length": 14726, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$235,:3,:14726"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_540_3_14723", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_540_3_14723.npy", "length": 14723, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$540,:3,:14723"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_843_3_14936", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_843_3_14936.npy", "length": 14936, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$843,:3,:14936"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_405_3_15332", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_405_3_15332.npy", "length": 15332, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$405,:3,:15332"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_640_3_14766", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_640_3_14766.npy", "length": 14766, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$640,:3,:14766"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_985_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_985_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$985,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_348_3_15511", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_348_3_15511.npy", "length": 15511, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$348,:3,:15511"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_23_3_15626", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_23_3_15626.npy", "length": 15626, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$23,:3,:15626"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_905_3_14917", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_905_3_14917.npy", "length": 14917, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$905,:3,:14917"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_934_3_15411", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_934_3_15411.npy", "length": 15411, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$934,:3,:15411"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_266_3_16073", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_266_3_16073.npy", "length": 16073, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$266,:3,:16073"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_546_3_14753", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_546_3_14753.npy", "length": 14753, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$546,:3,:14753"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_672_3_16373", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_672_3_16373.npy", "length": 16373, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$672,:3,:16373"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_890_3_15967", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_890_3_15967.npy", "length": 15967, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$890,:3,:15967"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_579_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_579_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$579,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_229_3_15789", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_229_3_15789.npy", "length": 15789, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$229,:3,:15789"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_755_3_15685", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_755_3_15685.npy", "length": 15685, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$755,:3,:15685"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_45_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_45_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$45,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_59_3_16045", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_59_3_16045.npy", "length": 16045, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$59,:3,:16045"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_926_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_926_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$926,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_822_3_15125", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_822_3_15125.npy", "length": 15125, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$822,:3,:15125"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_906_3_15917", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_906_3_15917.npy", "length": 15917, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$906,:3,:15917"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_312_3_15469", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_312_3_15469.npy", "length": 15469, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$312,:3,:15469"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_841_3_14673", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_841_3_14673.npy", "length": 14673, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$841,:3,:14673"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_268_3_15735", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_268_3_15735.npy", "length": 15735, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$268,:3,:15735"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_702_3_16640", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_702_3_16640.npy", "length": 16640, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$702,:3,:16640"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_545_3_15236", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_545_3_15236.npy", "length": 15236, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$545,:3,:15236"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_1022_3_15642", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_1022_3_15642.npy", "length": 15642, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$1022,:3,:15642"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_888_3_15310", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_888_3_15310.npy", "length": 15310, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$888,:3,:15310"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_586_3_16008", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_586_3_16008.npy", "length": 16008, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$586,:3,:16008"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_724_3_15728", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_724_3_15728.npy", "length": 15728, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$724,:3,:15728"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_559_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_559_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$559,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_1013_3_15830", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_1013_3_15830.npy", "length": 15830, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$1013,:3,:15830"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_172_3_14773", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_172_3_14773.npy", "length": 14773, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$172,:3,:14773"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_211_3_14563", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_211_3_14563.npy", "length": 14563, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$211,:3,:14563"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_542_3_15535", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_542_3_15535.npy", "length": 15535, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$542,:3,:15535"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_382_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_382_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$382,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_808_3_15723", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_808_3_15723.npy", "length": 15723, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$808,:3,:15723"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_133_3_15914", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_133_3_15914.npy", "length": 15914, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$133,:3,:15914"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_6_3_14856", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_6_3_14856.npy", "length": 14856, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$6,:3,:14856"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_954_3_15998", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_954_3_15998.npy", "length": 15998, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$954,:3,:15998"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_554_3_15274", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_554_3_15274.npy", "length": 15274, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$554,:3,:15274"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_185_3_14881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_185_3_14881.npy", "length": 14881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$185,:3,:14881"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_548_3_15645", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_548_3_15645.npy", "length": 15645, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$548,:3,:15645"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_468_3_16145", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_468_3_16145.npy", "length": 16145, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$468,:3,:16145"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_650_3_14860", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_650_3_14860.npy", "length": 14860, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$650,:3,:14860"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_423_3_17463", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_423_3_17463.npy", "length": 17463, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$423,:3,:17463"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_409_3_16080", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_409_3_16080.npy", "length": 16080, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$409,:3,:16080"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_284_3_14704", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_284_3_14704.npy", "length": 14704, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$284,:3,:14704"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_221_3_14948", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_221_3_14948.npy", "length": 14948, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$221,:3,:14948"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_60_3_16515", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_60_3_16515.npy", "length": 16515, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$60,:3,:16515"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_962_3_15420", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_962_3_15420.npy", "length": 15420, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$962,:3,:15420"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_471_3_15430", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_471_3_15430.npy", "length": 15430, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$471,:3,:15430"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_809_3_15382", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_809_3_15382.npy", "length": 15382, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$809,:3,:15382"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_319_3_15527", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_319_3_15527.npy", "length": 15527, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$319,:3,:15527"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_444_3_15281", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_444_3_15281.npy", "length": 15281, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$444,:3,:15281"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_369_3_16102", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_369_3_16102.npy", "length": 16102, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$369,:3,:16102"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_664_3_15035", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_664_3_15035.npy", "length": 15035, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$664,:3,:15035"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_96_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_96_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$96,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_763_3_15978", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_763_3_15978.npy", "length": 15978, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$763,:3,:15978"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_102_3_16044", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_102_3_16044.npy", "length": 16044, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$102,:3,:16044"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_766_3_14992", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_766_3_14992.npy", "length": 14992, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$766,:3,:14992"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_459_3_16761", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_459_3_16761.npy", "length": 16761, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$459,:3,:16761"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_148_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_148_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$148,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_591_3_17439", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_591_3_17439.npy", "length": 17439, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$591,:3,:17439"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_229_3_15722", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_229_3_15722.npy", "length": 15722, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$229,:3,:15722"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_42_3_15117", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_42_3_15117.npy", "length": 15117, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$42,:3,:15117"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_537_3_15739", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_537_3_15739.npy", "length": 15739, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$537,:3,:15739"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_173_3_15188", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_173_3_15188.npy", "length": 15188, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$173,:3,:15188"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_391_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_391_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$391,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_518_3_14962", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_518_3_14962.npy", "length": 14962, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$518,:3,:14962"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_19_3_15157", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_19_3_15157.npy", "length": 15157, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$19,:3,:15157"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_606_3_16524", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_606_3_16524.npy", "length": 16524, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket11$606,:3,:16524"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_483_3_16073", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_483_3_16073.npy", "length": 16073, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$483,:3,:16073"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_312_3_15839", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_312_3_15839.npy", "length": 15839, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$312,:3,:15839"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_496_3_14657", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_496_3_14657.npy", "length": 14657, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$496,:3,:14657"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_598_3_15284", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_598_3_15284.npy", "length": 15284, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$598,:3,:15284"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_920_3_14986", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_920_3_14986.npy", "length": 14986, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$920,:3,:14986"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_328_3_15435", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_328_3_15435.npy", "length": 15435, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$328,:3,:15435"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_698_3_17196", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_698_3_17196.npy", "length": 17196, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$698,:3,:17196"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_326_3_15551", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_326_3_15551.npy", "length": 15551, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$326,:3,:15551"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_171_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_171_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$171,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_197_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_197_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$197,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_416_3_16166", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_416_3_16166.npy", "length": 16166, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$416,:3,:16166"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_760_3_15322", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_760_3_15322.npy", "length": 15322, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$760,:3,:15322"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_281_3_15502", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_281_3_15502.npy", "length": 15502, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$281,:3,:15502"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_206_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_206_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$206,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_785_3_15595", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_785_3_15595.npy", "length": 15595, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$785,:3,:15595"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_498_3_16370", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_498_3_16370.npy", "length": 16370, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$498,:3,:16370"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_887_3_15214", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_887_3_15214.npy", "length": 15214, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$887,:3,:15214"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_925_3_14962", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_925_3_14962.npy", "length": 14962, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$925,:3,:14962"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_819_3_16101", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_819_3_16101.npy", "length": 16101, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$819,:3,:16101"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_995_3_15924", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_995_3_15924.npy", "length": 15924, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$995,:3,:15924"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_948_3_16208", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_948_3_16208.npy", "length": 16208, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$948,:3,:16208"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_1006_3_15235", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_1006_3_15235.npy", "length": 15235, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket1$1006,:3,:15235"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_556_3_16256", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_556_3_16256.npy", "length": 16256, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$556,:3,:16256"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_679_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_679_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$679,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_538_3_14736", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_538_3_14736.npy", "length": 14736, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$538,:3,:14736"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_673_3_15231", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_673_3_15231.npy", "length": 15231, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$673,:3,:15231"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_139_3_15473", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_139_3_15473.npy", "length": 15473, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$139,:3,:15473"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_214_3_15823", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_214_3_15823.npy", "length": 15823, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$214,:3,:15823"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_889_3_14686", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_889_3_14686.npy", "length": 14686, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$889,:3,:14686"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_550_3_15048", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_550_3_15048.npy", "length": 15048, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$550,:3,:15048"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_123_3_15260", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_123_3_15260.npy", "length": 15260, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$123,:3,:15260"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_227_3_15356", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_227_3_15356.npy", "length": 15356, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$227,:3,:15356"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_232_3_15594", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_232_3_15594.npy", "length": 15594, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$232,:3,:15594"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_979_3_14692", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_979_3_14692.npy", "length": 14692, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$979,:3,:14692"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_566_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_566_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$566,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_98_3_15398", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_98_3_15398.npy", "length": 15398, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$98,:3,:15398"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_836_3_16289", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_836_3_16289.npy", "length": 16289, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$836,:3,:16289"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_588_3_15538", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_588_3_15538.npy", "length": 15538, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$588,:3,:15538"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_830_3_16039", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_830_3_16039.npy", "length": 16039, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$830,:3,:16039"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_406_3_15531", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_406_3_15531.npy", "length": 15531, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$406,:3,:15531"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_161_3_14500", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_161_3_14500.npy", "length": 14500, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$161,:3,:14500"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_1004_3_16067", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_1004_3_16067.npy", "length": 16067, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$1004,:3,:16067"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_87_3_15238", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_87_3_15238.npy", "length": 15238, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$87,:3,:15238"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_346_3_16091", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_346_3_16091.npy", "length": 16091, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$346,:3,:16091"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_730_3_14598", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_730_3_14598.npy", "length": 14598, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$730,:3,:14598"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_269_3_15884", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_269_3_15884.npy", "length": 15884, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$269,:3,:15884"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_194_3_14667", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_194_3_14667.npy", "length": 14667, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$194,:3,:14667"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_37_3_16173", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_37_3_16173.npy", "length": 16173, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$37,:3,:16173"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_252_3_15881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_252_3_15881.npy", "length": 15881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$252,:3,:15881"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_232_3_15520", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_232_3_15520.npy", "length": 15520, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$232,:3,:15520"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_510_3_16053", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_510_3_16053.npy", "length": 16053, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$510,:3,:16053"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_751_3_15235", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_751_3_15235.npy", "length": 15235, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$751,:3,:15235"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_949_3_15164", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_949_3_15164.npy", "length": 15164, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$949,:3,:15164"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_425_3_15429", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_425_3_15429.npy", "length": 15429, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$425,:3,:15429"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_158_3_15677", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_158_3_15677.npy", "length": 15677, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$158,:3,:15677"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_23_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_23_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$23,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_240_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_240_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$240,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_28_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_28_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$28,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_216_3_15503", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_216_3_15503.npy", "length": 15503, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$216,:3,:15503"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_832_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_832_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$832,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_446_3_15517", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_446_3_15517.npy", "length": 15517, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$446,:3,:15517"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_1008_3_16536", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_1008_3_16536.npy", "length": 16536, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket11$1008,:3,:16536"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_428_3_14959", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_428_3_14959.npy", "length": 14959, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$428,:3,:14959"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_200_3_16232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_200_3_16232.npy", "length": 16232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$200,:3,:16232"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_369_3_15456", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_369_3_15456.npy", "length": 15456, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$369,:3,:15456"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_753_3_15956", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_753_3_15956.npy", "length": 15956, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$753,:3,:15956"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_401_3_14606", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_401_3_14606.npy", "length": 14606, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$401,:3,:14606"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_992_3_15664", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_992_3_15664.npy", "length": 15664, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$992,:3,:15664"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_456_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_456_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$456,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_616_3_15675", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_616_3_15675.npy", "length": 15675, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$616,:3,:15675"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_850_3_15041", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_850_3_15041.npy", "length": 15041, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$850,:3,:15041"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_914_3_15562", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_914_3_15562.npy", "length": 15562, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$914,:3,:15562"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_20_3_15951", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_20_3_15951.npy", "length": 15951, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$20,:3,:15951"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_253_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_253_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$253,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_110_3_16239", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_110_3_16239.npy", "length": 16239, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$110,:3,:16239"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_884_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_884_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$884,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_800_3_15337", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_800_3_15337.npy", "length": 15337, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$800,:3,:15337"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_606_3_15756", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_606_3_15756.npy", "length": 15756, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$606,:3,:15756"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_75_3_15742", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_75_3_15742.npy", "length": 15742, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$75,:3,:15742"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_196_3_15051", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_196_3_15051.npy", "length": 15051, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$196,:3,:15051"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_944_3_16217", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_944_3_16217.npy", "length": 16217, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$944,:3,:16217"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_824_3_14841", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_824_3_14841.npy", "length": 14841, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$824,:3,:14841"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_50_3_15037", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_50_3_15037.npy", "length": 15037, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$50,:3,:15037"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_771_3_15036", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_771_3_15036.npy", "length": 15036, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$771,:3,:15036"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_605_3_15562", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_605_3_15562.npy", "length": 15562, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$605,:3,:15562"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_184_3_15925", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_184_3_15925.npy", "length": 15925, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$184,:3,:15925"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_988_3_15830", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_988_3_15830.npy", "length": 15830, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$988,:3,:15830"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_447_3_15045", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_447_3_15045.npy", "length": 15045, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$447,:3,:15045"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_64_3_14902", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_64_3_14902.npy", "length": 14902, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$64,:3,:14902"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_811_3_14758", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_811_3_14758.npy", "length": 14758, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$811,:3,:14758"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_916_3_15114", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_916_3_15114.npy", "length": 15114, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$916,:3,:15114"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_752_3_15695", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_752_3_15695.npy", "length": 15695, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$752,:3,:15695"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_101_3_15317", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_101_3_15317.npy", "length": 15317, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$101,:3,:15317"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_893_3_15717", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_893_3_15717.npy", "length": 15717, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$893,:3,:15717"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_711_3_15473", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_711_3_15473.npy", "length": 15473, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$711,:3,:15473"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_381_3_16561", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_381_3_16561.npy", "length": 16561, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$381,:3,:16561"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_989_3_15152", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_989_3_15152.npy", "length": 15152, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$989,:3,:15152"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_489_3_14814", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_489_3_14814.npy", "length": 14814, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$489,:3,:14814"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_487_3_15398", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_487_3_15398.npy", "length": 15398, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$487,:3,:15398"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_915_3_14978", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_915_3_14978.npy", "length": 14978, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$915,:3,:14978"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_422_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_422_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$422,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_119_3_14576", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_119_3_14576.npy", "length": 14576, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$119,:3,:14576"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_709_3_16402", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_709_3_16402.npy", "length": 16402, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$709,:3,:16402"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_26_3_15417", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_26_3_15417.npy", "length": 15417, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$26,:3,:15417"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_989_3_15720", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_989_3_15720.npy", "length": 15720, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$989,:3,:15720"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_1007_3_16353", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_1007_3_16353.npy", "length": 16353, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$1007,:3,:16353"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_432_3_15264", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_432_3_15264.npy", "length": 15264, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$432,:3,:15264"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_471_3_14916", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_471_3_14916.npy", "length": 14916, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$471,:3,:14916"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_317_3_15169", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_317_3_15169.npy", "length": 15169, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$317,:3,:15169"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_226_3_15463", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_226_3_15463.npy", "length": 15463, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$226,:3,:15463"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_133_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_133_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$133,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_98_3_15095", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_98_3_15095.npy", "length": 15095, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$98,:3,:15095"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_379_3_15609", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_379_3_15609.npy", "length": 15609, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$379,:3,:15609"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_65_3_16067", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_65_3_16067.npy", "length": 16067, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$65,:3,:16067"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_157_3_16289", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_157_3_16289.npy", "length": 16289, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$157,:3,:16289"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_564_3_15192", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_564_3_15192.npy", "length": 15192, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$564,:3,:15192"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_571_3_15491", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_571_3_15491.npy", "length": 15491, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$571,:3,:15491"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_761_3_16544", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_761_3_16544.npy", "length": 16544, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$761,:3,:16544"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_82_3_15727", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_82_3_15727.npy", "length": 15727, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket4$82,:3,:15727"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_770_3_16187", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_770_3_16187.npy", "length": 16187, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$770,:3,:16187"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_231_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_231_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$231,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_33_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_33_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$33,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_729_3_15130", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_729_3_15130.npy", "length": 15130, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$729,:3,:15130"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_838_3_15586", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_838_3_15586.npy", "length": 15586, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$838,:3,:15586"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_460_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_460_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$460,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_596_3_15897", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_596_3_15897.npy", "length": 15897, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$596,:3,:15897"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_781_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_781_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$781,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_150_3_14675", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_150_3_14675.npy", "length": 14675, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$150,:3,:14675"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_152_3_14526", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_152_3_14526.npy", "length": 14526, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$152,:3,:14526"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_262_3_16148", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_262_3_16148.npy", "length": 16148, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$262,:3,:16148"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_163_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_163_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$163,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_97_3_14807", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_97_3_14807.npy", "length": 14807, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$97,:3,:14807"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_762_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_762_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$762,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_852_3_15545", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_852_3_15545.npy", "length": 15545, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$852,:3,:15545"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_499_3_15269", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_499_3_15269.npy", "length": 15269, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$499,:3,:15269"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_462_3_16085", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_462_3_16085.npy", "length": 16085, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$462,:3,:16085"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_353_3_15250", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_353_3_15250.npy", "length": 15250, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$353,:3,:15250"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_265_3_15648", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_265_3_15648.npy", "length": 15648, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$265,:3,:15648"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_303_3_16570", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_303_3_16570.npy", "length": 16570, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$303,:3,:16570"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_35_3_14542", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_35_3_14542.npy", "length": 14542, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$35,:3,:14542"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_919_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_919_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$919,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_157_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_157_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$157,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_887_3_15356", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_887_3_15356.npy", "length": 15356, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$887,:3,:15356"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_698_3_15861", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_698_3_15861.npy", "length": 15861, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$698,:3,:15861"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_19_3_15563", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_19_3_15563.npy", "length": 15563, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$19,:3,:15563"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_305_3_15348", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_305_3_15348.npy", "length": 15348, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$305,:3,:15348"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_501_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_501_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$501,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_994_3_15541", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_994_3_15541.npy", "length": 15541, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$994,:3,:15541"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_322_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_322_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$322,:3,:14001"}}
+{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_791_3_15600", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_791_3_15600.npy", "length": 15600, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$791,:3,:15600"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_158", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_158.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_687", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_687.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_807", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_807.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_63", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_63.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1064", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1064.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_141", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_141.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_26", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_26.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_231", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_231.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1200", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1200.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1194", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1194.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_930", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_930.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_937", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_937.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_890", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_890.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_662", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_662.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_852", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_852.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_328", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_328.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_705", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_705.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_56", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_56.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_841", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_841.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_771", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_771.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_243", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_243.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1040", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1040.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_5", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_5.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1291", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1291.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_668", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_668.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_734", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_734.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_979", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_979.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_481", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_481.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1134", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1134.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_468", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_468.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_353", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_353.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_8", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_8.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_114", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_114.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_134", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_134.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_794", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_794.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_679", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_679.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1078", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1078.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1131", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1131.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_518", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_518.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1111", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1111.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_470", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_470.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_830", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_830.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_164", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_164.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1014", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1014.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_203", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_203.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1390", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1390.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1013", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1013.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_45", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_45.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_244", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_244.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_437", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_437.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1254", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1254.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1459", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1459.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_310", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_310.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1306", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1306.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_616", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_616.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_83", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_83.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_159", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_159.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1047", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1047.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_860", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_860.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_80", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_80.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_487", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_487.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1298", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1298.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_73", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_73.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_243", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_243.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1314", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1314.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_248", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_248.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_967", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_967.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1088", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1088.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_191", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_191.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_242", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_242.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_522", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_522.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_733", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_733.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_848", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_848.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_946", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_946.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_124", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_124.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1001", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1001.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_257", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_257.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_79", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_79.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_583", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_583.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_754", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_754.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_40", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_40.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_245", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_245.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_750", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_750.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_641", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_641.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1283", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1283.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_419", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_419.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_426", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_426.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_211", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_211.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_330", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_330.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_988", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_988.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1339", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1339.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_635", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_635.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_85", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_85.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_922", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_922.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_905", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_905.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_19", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_19.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_322", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_322.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_906", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_906.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_887", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_887.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1478", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1478.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_281", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_281.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1059", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1059.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1218", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1218.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_332", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_332.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_13", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_13.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_844", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_844.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_235", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_235.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_247", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_247.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_159", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_159.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_171", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_171.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_45", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_45.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1068", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1068.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_585", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_585.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1274", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1274.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_424", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_424.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_32", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_32.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_130", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_130.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_703", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_703.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_449", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_449.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_254", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_254.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1106", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1106.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_792", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_792.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1483", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1483.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_164", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_164.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_841", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_841.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_742", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_742.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_76", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_76.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_376", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_376.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_116", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_116.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1104", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1104.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1063", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1063.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1105", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1105.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1254", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1254.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_369", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_369.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_833", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_833.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_152", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_152.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_407", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_407.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_749", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_749.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_199", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_199.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1210", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1210.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_627", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_627.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_450", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_450.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_72", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_72.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_251", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_251.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1116", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1116.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_110", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_110.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_877", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_877.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_924", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_924.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_959", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_959.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_108", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_108.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_407", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_407.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1238", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1238.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_338", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_338.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_994", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_994.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_734", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_734.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1306", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1306.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_117", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_117.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_579", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_579.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1175", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1175.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1163", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1163.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_152", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_152.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1299", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1299.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_98", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_98.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_475", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_475.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_167", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_167.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1154", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1154.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_162", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_162.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1012", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1012.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_318", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_318.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_992", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_992.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1074", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1074.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_114", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_114.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_260", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_260.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_50", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_50.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_581", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_581.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1448", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1448.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_292", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_292.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_22", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_22.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_614", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_614.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_441", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_441.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_0", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_0.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_240", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_240.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_421", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_421.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_62", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_62.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1226", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1226.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1023", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1023.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_41", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_41.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_940", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_940.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_416", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_416.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_25", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_25.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_463", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_463.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_241", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_241.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_476", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_476.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_345", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_345.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_291", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_291.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1450", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1450.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_88", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_88.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_172", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_172.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_447", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_447.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1454", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1454.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_513", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_513.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_689", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_689.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_314", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_314.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_432", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_432.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1174", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1174.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_900", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_900.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_923", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_923.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_635", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_635.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_955", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_955.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_582", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_582.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_697", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_697.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_206", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_206.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_81", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_81.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_889", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_889.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_366", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_366.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1260", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1260.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_627", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_627.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_146", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_146.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_562", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_562.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_739", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_739.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_178", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_178.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_791", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_791.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_308", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_308.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_75", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_75.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1133", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1133.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_590", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_590.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_594", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_594.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_596", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_596.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_287", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_287.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_756", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_756.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_13", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_13.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_441", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_441.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_12", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_12.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_227", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_227.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_471", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_471.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1316", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1316.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_985", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_985.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_279", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_279.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_211", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_211.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_444", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_444.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1262", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1262.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_349", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_349.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_217", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_217.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_220", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_220.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_152", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_152.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_939", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_939.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_851", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_851.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1371", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1371.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1430", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1430.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_211", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_211.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_643", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_643.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1159", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1159.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1285", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1285.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_677", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_677.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1352", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1352.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_264", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_264.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1395", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1395.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_851", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_851.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_886", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_886.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_390", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_390.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_163", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_163.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1083", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1083.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_316", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_316.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_815", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_815.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1027", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1027.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1208", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1208.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_377", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_377.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_205", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_205.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_798", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_798.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_504", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_504.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_762", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_762.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_697", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_697.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_329", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_329.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1228", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1228.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_258", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_258.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_324", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_324.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_49", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_49.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_938", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_938.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_37", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_37.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1341", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1341.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_289", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_289.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_880", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_880.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1017", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1017.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_105", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_105.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_650", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_650.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_313", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_313.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1033", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1033.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_184", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_184.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1199", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1199.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_168", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_168.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1273", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1273.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_259", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_259.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1245", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1245.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1449", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1449.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1307", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1307.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_859", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_859.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1418", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1418.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_439", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_439.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_35", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_35.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_277", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_277.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_278", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_278.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_692", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_692.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1021", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1021.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_223", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_223.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_170", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_170.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_838", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_838.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_53", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_53.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_312", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_312.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1095", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1095.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_473", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_473.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1291", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1291.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1238", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1238.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1034", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1034.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_272", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_272.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_133", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_133.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_336", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_336.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_765", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_765.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_295", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_295.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_773", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_773.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1176", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1176.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_8", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_8.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_76", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_76.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_496", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_496.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_124", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_124.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_155", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_155.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_111", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_111.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1004", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1004.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_707", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_707.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_728", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_728.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_406", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_406.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_925", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_925.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1438", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1438.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_250", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_250.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_216", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_216.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_135", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_135.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_432", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_432.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1030", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1030.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1201", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1201.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_447", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_447.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_63", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_63.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_497", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_497.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_132", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_132.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_268", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_268.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1089", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1089.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_234", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_234.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_208", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_208.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_82", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_82.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1326", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1326.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_62", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_62.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_71", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_71.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_873", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_873.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_294", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_294.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_358", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_358.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_648", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_648.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1284", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1284.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1347", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1347.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
+{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_360", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_360.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_188", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_188.wav", "length": 19211, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_048.wav", "length": 21141, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.64 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_059", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_059.wav", "length": 21431, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.68 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_103", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_103.wav", "length": 17746, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.22 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_183.wav", "length": 20143, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.52 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_118", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_118.wav", "length": 17897, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.24 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_071", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_071.wav", "length": 21517, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_167", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_167.wav", "length": 24123, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.02 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_118", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_118.wav", "length": 20919, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_044", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_044.wav", "length": 20884, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_200", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_200.wav", "length": 19242, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.41 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_022", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_022.wav", "length": 16703, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_013", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_013.wav", "length": 21047, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_133", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_133.wav", "length": 18979, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.37 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_154.wav", "length": 26577, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.32 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_054.wav", "length": 21026, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_079", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_079.wav", "length": 17460, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_070", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_070.wav", "length": 20468, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.56 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_048.wav", "length": 27417, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_145", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_145.wav", "length": 17575, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_026", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_026.wav", "length": 20730, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.59 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_075.wav", "length": 16628, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.08 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_038", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_038.wav", "length": 21493, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_012.wav", "length": 20674, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.58 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_119", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_119.wav", "length": 20990, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.62 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_134", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_134.wav", "length": 24056, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_003.wav", "length": 23576, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.95 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_123.wav", "length": 17680, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_145", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_145.wav", "length": 18560, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.32 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_097", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_097.wav", "length": 20973, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.62 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_139", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_139.wav", "length": 18577, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.32 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_026", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_026.wav", "length": 20797, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_148", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_148.wav", "length": 23115, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.89 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_085", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_085.wav", "length": 20524, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.57 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_176", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_176.wav", "length": 17584, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_058", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_058.wav", "length": 17798, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.22 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_113", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_113.wav", "length": 17685, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_122", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_122.wav", "length": 20344, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_020", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_020.wav", "length": 13855, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.73 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_163", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_163.wav", "length": 16329, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.04 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_152", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_152.wav", "length": 15922, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_006.wav", "length": 13750, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.72 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_181", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_181.wav", "length": 20143, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.52 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_153.wav", "length": 26367, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.30 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_154.wav", "length": 16140, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.02 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_094", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_094.wav", "length": 21669, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.71 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_144.wav", "length": 16091, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_049", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_049.wav", "length": 21481, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_146", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_146.wav", "length": 19171, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_143", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_143.wav", "length": 23890, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_092", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_092.wav", "length": 21934, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.74 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_176", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_176.wav", "length": 24067, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_137", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_137.wav", "length": 19281, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.41 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_091", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_091.wav", "length": 19394, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_019", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_019.wav", "length": 20933, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.62 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_070", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_070.wav", "length": 16411, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.05 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_095", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_095.wav", "length": 23539, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_165.wav", "length": 16806, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.10 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_095", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_095.wav", "length": 20424, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.55 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_121", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_121.wav", "length": 23442, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.93 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_001", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_001.wav", "length": 22311, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.79 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_010", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_010.wav", "length": 16744, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_190", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_190.wav", "length": 16626, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.08 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_126.wav", "length": 23797, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.97 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_116", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_116.wav", "length": 20774, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_189", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_189.wav", "length": 21362, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.67 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_160", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_160.wav", "length": 23058, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.88 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_171", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_171.wav", "length": 17420, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_044", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_044.wav", "length": 16088, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_109", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_109.wav", "length": 19796, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.47 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_117.wav", "length": 19903, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.49 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_076", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_076.wav", "length": 23370, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.92 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_049", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_049.wav", "length": 23420, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.93 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_065", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_065.wav", "length": 23688, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.96 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_106", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_106.wav", "length": 19346, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_060.wav", "length": 17576, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_102.wav", "length": 20654, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.58 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_179", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_179.wav", "length": 16669, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.08 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_166", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_166.wav", "length": 23894, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_101", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_101.wav", "length": 19419, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_127", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_127.wav", "length": 18842, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.36 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_110", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_110.wav", "length": 19729, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.47 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_125", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_125.wav", "length": 16409, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.05 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_161", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_161.wav", "length": 16694, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_003.wav", "length": 16933, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_129.wav", "length": 23928, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_144.wav", "length": 22707, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.84 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_102.wav", "length": 15507, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_178", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_178.wav", "length": 17675, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_060.wav", "length": 23392, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.92 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_107", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_107.wav", "length": 20757, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.59 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_091", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_091.wav", "length": 21502, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_135.wav", "length": 17258, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.16 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_059", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_059.wav", "length": 23421, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.93 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_163", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_163.wav", "length": 17123, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.14 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_130", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_130.wav", "length": 17707, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_019", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_019.wav", "length": 16734, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_195.wav", "length": 19680, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.46 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_157", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_157.wav", "length": 18839, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.35 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_015.wav", "length": 23211, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.90 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_008", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_008.wav", "length": 16874, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_029", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_029.wav", "length": 16480, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.06 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_097", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_097.wav", "length": 19201, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_158", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_158.wav", "length": 23289, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.91 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_011", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_011.wav", "length": 19571, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.45 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_052", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_052.wav", "length": 18121, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.27 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_095", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_095.wav", "length": 21042, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_105.wav", "length": 21041, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_194", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_194.wav", "length": 15027, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.88 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_125", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_125.wav", "length": 21012, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_162.wav", "length": 19038, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.38 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_029", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_029.wav", "length": 17592, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_009.wav", "length": 20898, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_115", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_115.wav", "length": 21119, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.64 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_187", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_187.wav", "length": 18970, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.37 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_100", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_100.wav", "length": 24064, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_057.wav", "length": 19135, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.39 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_104", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_104.wav", "length": 20832, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_164", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_164.wav", "length": 23124, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.89 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_033.wav", "length": 16884, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_003.wav", "length": 16914, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_078.wav", "length": 17834, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_106", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_106.wav", "length": 21041, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_088", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_088.wav", "length": 24277, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.03 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_181", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_181.wav", "length": 20296, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_132.wav", "length": 23003, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.88 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_084.wav", "length": 20896, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_010", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_010.wav", "length": 20899, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_112", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_112.wav", "length": 20496, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.56 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_078.wav", "length": 20209, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.53 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_160", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_160.wav", "length": 18974, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.37 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_030.wav", "length": 15978, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.00 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_193", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_193.wav", "length": 14326, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.79 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_094", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_094.wav", "length": 24045, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_145", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_145.wav", "length": 23945, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_173", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_173.wav", "length": 17832, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_034", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_034.wav", "length": 21611, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.70 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_133", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_133.wav", "length": 16305, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.04 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_180.wav", "length": 16930, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_185", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_185.wav", "length": 17585, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_144.wav", "length": 23986, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.00 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_109", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_109.wav", "length": 19356, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_164", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_164.wav", "length": 16931, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_138.wav", "length": 23124, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.89 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_179", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_179.wav", "length": 23759, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.97 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_172", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_172.wav", "length": 19324, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_104", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_104.wav", "length": 17811, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_137", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_137.wav", "length": 23968, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.00 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_028", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_028.wav", "length": 17653, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_149", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_149.wav", "length": 23948, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_133", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_133.wav", "length": 23886, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_025", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_025.wav", "length": 16824, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.10 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_152", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_152.wav", "length": 23289, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.91 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_010", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_010.wav", "length": 13327, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.67 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_072.wav", "length": 23335, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.92 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_198.wav", "length": 16539, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.07 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_096.wav", "length": 20839, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_191", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_191.wav", "length": 16848, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_150.wav", "length": 17966, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.25 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_036.wav", "length": 20897, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_149", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_149.wav", "length": 18910, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.36 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_013", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_013.wav", "length": 19513, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.44 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_111.wav", "length": 19284, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.41 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_170", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_170.wav", "length": 24046, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_154.wav", "length": 23751, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.97 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_072.wav", "length": 20306, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_122", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_122.wav", "length": 23501, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_165.wav", "length": 23826, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.98 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_085", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_085.wav", "length": 21450, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.68 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_139", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_139.wav", "length": 18895, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.36 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_130", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_130.wav", "length": 19213, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_046", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_046.wav", "length": 21533, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_043", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_043.wav", "length": 21246, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.66 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_169", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_169.wav", "length": 23301, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.91 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_106", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_106.wav", "length": 21751, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.72 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_186.wav", "length": 20405, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.55 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_004", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_004.wav", "length": 16696, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_055", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_055.wav", "length": 21537, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_184", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_184.wav", "length": 16710, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_170", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_170.wav", "length": 22604, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.83 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_044", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_044.wav", "length": 19443, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_065", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_065.wav", "length": 17365, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.17 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_111.wav", "length": 17811, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_080", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_080.wav", "length": 21616, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.70 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_074", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_074.wav", "length": 20353, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_031", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_031.wav", "length": 17459, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_014", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_014.wav", "length": 20724, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.59 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_035", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_035.wav", "length": 20863, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_014", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_014.wav", "length": 16744, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_172", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_172.wav", "length": 17003, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.13 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_015.wav", "length": 14805, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.85 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_063.wav", "length": 20813, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_093", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_093.wav", "length": 23495, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_135.wav", "length": 23824, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.98 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_086", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_086.wav", "length": 16421, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.05 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_096.wav", "length": 19418, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_023", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_023.wav", "length": 16933, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_154.wav", "length": 17446, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_137", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_137.wav", "length": 17366, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.17 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
+{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_118", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_118.wav", "length": 20374, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.55 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_AV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_PV.wav", "length": 122560, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.64 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_TV.wav", "length": 81984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_MV.wav", "length": 85120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_AV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_PV.wav", "length": 46720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_TV.wav", "length": 56384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_MV.wav", "length": 90368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_AV.wav", "length": 78208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_PV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_TV.wav", "length": 62976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_MV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49618_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49618_AV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49618_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49618_MV.wav", "length": 148352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_AV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_PV.wav", "length": 118208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_TV.wav", "length": 121600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_MV.wav", "length": 123584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_AV.wav", "length": 116608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_PV.wav", "length": 102784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_TV.wav", "length": 126336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_MV.wav", "length": 185984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 46.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49631_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49631_AV.wav", "length": 144768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49631_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49631_MV.wav", "length": 120000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49638_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49638_AV.wav", "length": 166784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 41.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49638_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49638_PV.wav", "length": 100224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49638_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49638_MV.wav", "length": 85824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_AV.wav", "length": 117376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_PV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_TV.wav", "length": 123968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_MV.wav", "length": 135552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49704_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49704_AV.wav", "length": 127168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49704_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49704_MV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_AV.wav", "length": 152384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_PV.wav", "length": 141184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_TV.wav", "length": 141376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_MV.wav", "length": 143424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49761_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49761_AV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49761_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49761_MV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49808_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49808_PV.wav", "length": 77824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49808_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49808_TV.wav", "length": 58624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49808_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49808_MV.wav", "length": 62208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49832_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49832_AV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49832_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49832_MV.wav", "length": 123776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49838_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49838_MV.wav", "length": 111552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_AV.wav", "length": 127552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_PV.wav", "length": 103552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_TV.wav", "length": 107968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_MV.wav", "length": 103040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_AV.wav", "length": 121344, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_PV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_TV.wav", "length": 124224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_MV.wav", "length": 124992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49931_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49931_AV.wav", "length": 127040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49931_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49931_PV.wav", "length": 126976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49931_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49931_TV.wav", "length": 131840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49968_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49968_PV.wav", "length": 71808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49968_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49968_TV.wav", "length": 71360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49968_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49968_MV.wav", "length": 87168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49978_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49978_PV.wav", "length": 59136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49986_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49986_AV.wav", "length": 102336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49986_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49986_MV.wav", "length": 68224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49989_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49989_PV.wav", "length": 30208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49989_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49989_MV.wav", "length": 57920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50012_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50012_MV.wav", "length": 87040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_AV.wav", "length": 118656, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_PV.wav", "length": 122624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_TV.wav", "length": 120768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_MV.wav", "length": 125376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50026_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50026_AV.wav", "length": 136384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50026_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50026_MV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50030_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50030_PV.wav", "length": 70080, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.52 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50030_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50030_TV.wav", "length": 61184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50030_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50030_MV.wav", "length": 87040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50043_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50043_AV.wav", "length": 108608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50043_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50043_PV.wav", "length": 98368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50043_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50043_TV.wav", "length": 153984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50048_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50048_PV.wav", "length": 129407, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50048_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50048_TV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50048_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50048_MV.wav", "length": 199936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 49.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50066_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50066_AV.wav", "length": 121920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50066_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50066_MV.wav", "length": 136640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50067_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50067_PV.wav", "length": 122240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50067_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50067_TV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50067_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50067_MV.wav", "length": 124736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_AV.wav", "length": 63360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_MV.wav", "length": 91840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50100_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50100_AV.wav", "length": 131392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50100_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50100_MV.wav", "length": 155136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_AV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_PV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_TV.wav", "length": 113600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_MV.wav", "length": 115968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_AV.wav", "length": 115904, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_PV.wav", "length": 83584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_TV.wav", "length": 97536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_MV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50137_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50137_PV.wav", "length": 110720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50137_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50137_TV.wav", "length": 115200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50137_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50137_MV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50138_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50138_AV.wav", "length": 121408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50138_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50138_MV.wav", "length": 123584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50164_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50164_PV.wav", "length": 119936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50164_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50164_TV.wav", "length": 134016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50164_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50164_MV.wav", "length": 123456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50204_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50204_AV.wav", "length": 123968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50204_MV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50204_MV_1.wav", "length": 67584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50204_MV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50204_MV_2.wav", "length": 148992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50206_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50206_PV.wav", "length": 91136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50206_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50206_TV.wav", "length": 81920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50206_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50206_MV.wav", "length": 77568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50217_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50217_AV.wav", "length": 124992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50217_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50217_PV.wav", "length": 127168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_AV.wav", "length": 91840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_PV.wav", "length": 66752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_TV.wav", "length": 67392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_MV.wav", "length": 96192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50219_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50219_AV.wav", "length": 140352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50219_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50219_PV.wav", "length": 64384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50219_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50219_MV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_AV.wav", "length": 131392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_PV.wav", "length": 112640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_TV.wav", "length": 153216, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_MV.wav", "length": 125184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_AV.wav", "length": 117568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_PV.wav", "length": 110784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_TV.wav", "length": 118208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_MV.wav", "length": 161536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 40.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_AV.wav", "length": 134592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_PV.wav", "length": 118784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_TV.wav", "length": 125376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_MV.wav", "length": 127552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50247_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50247_AV.wav", "length": 108608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50247_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50247_PV.wav", "length": 125184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50247_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50247_MV.wav", "length": 134208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50248_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50248_TV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50249_AV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50249_AV_1.wav", "length": 62016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50249_AV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50249_AV_2.wav", "length": 74240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50249_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50249_MV.wav", "length": 121792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_AV.wav", "length": 108224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_PV.wav", "length": 115968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_TV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_TV_1.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_TV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_TV_2.wav", "length": 90752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_MV.wav", "length": 117952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_AV.wav", "length": 110016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_PV.wav", "length": 115200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_TV.wav", "length": 107008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_MV.wav", "length": 129151, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50280_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50280_AV.wav", "length": 100032, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.01 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50280_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50280_PV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50280_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50280_TV.wav", "length": 149056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_AV.wav", "length": 113408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_PV.wav", "length": 102720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_TV.wav", "length": 120640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_MV.wav", "length": 118976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50291_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50291_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50291_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50291_PV.wav", "length": 111616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50291_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50291_MV.wav", "length": 71168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_PV.wav", "length": 125440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_TV.wav", "length": 157568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 39.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_MV.wav", "length": 153536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50300_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50300_AV.wav", "length": 115520, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.88 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50300_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50300_MV.wav", "length": 111936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_AV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_AV_1.wav", "length": 210752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 52.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_AV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_AV_2.wav", "length": 123392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_MV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_MV_1.wav", "length": 20992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 5.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_MV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_MV_2.wav", "length": 142208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_AV.wav", "length": 123968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_PV.wav", "length": 149440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_TV.wav", "length": 121920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_MV.wav", "length": 162816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 40.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_AV.wav", "length": 110784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_PV.wav", "length": 111552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_TV.wav", "length": 110976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_MV.wav", "length": 114816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50335_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50335_AV.wav", "length": 117824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50335_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50335_MV.wav", "length": 123840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50341_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50341_AV.wav", "length": 89792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50341_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50341_MV.wav", "length": 135168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_AV.wav", "length": 147840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_PV.wav", "length": 123136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_TV.wav", "length": 122560, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.64 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_MV.wav", "length": 122368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50640_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50640_MV.wav", "length": 129151, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50644_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50644_AV.wav", "length": 119808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50644_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50644_MV.wav", "length": 121600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50646_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50646_AV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50646_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50646_MV.wav", "length": 134784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_AV.wav", "length": 119424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_PV.wav", "length": 120960, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.24 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_TV.wav", "length": 120640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_MV.wav", "length": 123392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50652_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50652_AV.wav", "length": 118400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50652_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50652_MV.wav", "length": 122368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50655_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50655_AV.wav", "length": 118784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50655_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50655_MV.wav", "length": 139200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_AV.wav", "length": 123392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_PV.wav", "length": 122752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_TV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_MV.wav", "length": 123456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_AV.wav", "length": 108992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_PV.wav", "length": 116992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_TV.wav", "length": 116608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_MV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_AV.wav", "length": 123584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_PV.wav", "length": 123776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_TV.wav", "length": 117440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_MV.wav", "length": 118720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_AV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_PV.wav", "length": 117824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_TV.wav", "length": 115584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_MV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_AV.wav", "length": 124160, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.04 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_PV.wav", "length": 122368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_TV.wav", "length": 115840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_MV.wav", "length": 123776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_AV.wav", "length": 121024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_PV.wav", "length": 124800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_TV.wav", "length": 123200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_MV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_MV_1.wav", "length": 35584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_MV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_MV_2.wav", "length": 121408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_AV.wav", "length": 116352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_PV.wav", "length": 131008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_TV.wav", "length": 119552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_MV.wav", "length": 130752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50707_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50707_AV.wav", "length": 125760, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.44 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50707_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50707_TV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50707_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50707_MV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_AV.wav", "length": 120000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_PV.wav", "length": 118720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_TV.wav", "length": 118016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_MV.wav", "length": 120192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50720_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50720_AV.wav", "length": 128768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50720_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50720_MV.wav", "length": 143552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_AV.wav", "length": 124608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_PV.wav", "length": 122752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_TV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_MV.wav", "length": 121920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_AV.wav", "length": 157760, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 39.44 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_PV.wav", "length": 150976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_TV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_MV.wav", "length": 130368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_AV.wav", "length": 92608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_PV.wav", "length": 89920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_TV.wav", "length": 97408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_MV.wav", "length": 116608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50743_AV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50743_AV_1.wav", "length": 106944, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50743_AV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50743_AV_2.wav", "length": 27072, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 6.77 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50743_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50743_MV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_AV.wav", "length": 116352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_PV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_TV.wav", "length": 118592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_MV.wav", "length": 115584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_AV.wav", "length": 113984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_PV.wav", "length": 109376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_TV.wav", "length": 101376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_MV.wav", "length": 113792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_AV.wav", "length": 120768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_PV.wav", "length": 122176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_TV.wav", "length": 124608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_MV.wav", "length": 128000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50774_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50774_AV.wav", "length": 121024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50774_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50774_TV.wav", "length": 117440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50774_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50774_MV.wav", "length": 122816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_AV.wav", "length": 116224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_PV.wav", "length": 117568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_TV.wav", "length": 118592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_MV.wav", "length": 119616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_AV.wav", "length": 116992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_PV.wav", "length": 123200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_TV.wav", "length": 119040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_MV.wav", "length": 121152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50790_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50790_AV.wav", "length": 112384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50790_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50790_MV.wav", "length": 116416, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_PV.wav", "length": 116800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_TV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_TV_1.wav", "length": 37632, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.41 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_TV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_TV_2.wav", "length": 116160, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.04 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_MV.wav", "length": 119552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_PV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_TV.wav", "length": 117824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_MV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_AV.wav", "length": 121856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_PV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_TV.wav", "length": 187968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 46.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_MV.wav", "length": 119936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_AV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_PV.wav", "length": 78400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_TV.wav", "length": 76992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_MV.wav", "length": 88192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "59536_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/59536_AV.wav", "length": 93184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "59536_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/59536_MV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_AV.wav", "length": 76224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_PV.wav", "length": 94400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_MV.wav", "length": 76992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_AV.wav", "length": 36992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_PV.wav", "length": 49152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_TV.wav", "length": 32000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_MV.wav", "length": 46656, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_AV.wav", "length": 50752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_PV.wav", "length": 55360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_TV.wav", "length": 35008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_MV.wav", "length": 45184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_AV.wav", "length": 36608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_PV.wav", "length": 66752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_TV.wav", "length": 41984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_MV.wav", "length": 47808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_AV.wav", "length": 42368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_PV.wav", "length": 58368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_TV.wav", "length": 52352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_MV.wav", "length": 64768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_AV.wav", "length": 42368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_PV.wav", "length": 52800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_TV.wav", "length": 44224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_MV.wav", "length": 59968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68395_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68395_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68395_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68395_TV.wav", "length": 85376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_AV.wav", "length": 58176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_PV.wav", "length": 64768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_TV.wav", "length": 60736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_MV.wav", "length": 60736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_AV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_PV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_TV.wav", "length": 83200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_MV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_AV.wav", "length": 64384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_PV.wav", "length": 75392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_TV.wav", "length": 60608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_MV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_AV.wav", "length": 86720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_PV.wav", "length": 69184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_TV.wav", "length": 84736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_MV.wav", "length": 102720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_AV.wav", "length": 46784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_PV.wav", "length": 54784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_TV.wav", "length": 43200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_MV.wav", "length": 70336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_AV.wav", "length": 55040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_PV.wav", "length": 34752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_TV.wav", "length": 32768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_MV.wav", "length": 53440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_AV.wav", "length": 84736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_PV.wav", "length": 88384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_TV.wav", "length": 127040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_MV.wav", "length": 91136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68556_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68556_AV.wav", "length": 56256, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68556_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68556_MV.wav", "length": 37824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_AV.wav", "length": 52224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_PV.wav", "length": 60224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_TV.wav", "length": 48768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_MV.wav", "length": 137216, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68864_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68864_PV.wav", "length": 72192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68864_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68864_TV.wav", "length": 70336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68864_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68864_MV.wav", "length": 105536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_AV.wav", "length": 45824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_PV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_TV.wav", "length": 63936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_MV.wav", "length": 64191, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_AV.wav", "length": 56576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_PV.wav", "length": 46592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_TV.wav", "length": 49792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_MV.wav", "length": 71616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_AV.wav", "length": 84608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_PV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_TV.wav", "length": 116992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_MV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_AV.wav", "length": 105152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_PV.wav", "length": 99136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_TV.wav", "length": 98752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_MV.wav", "length": 84608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_AV.wav", "length": 37824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_PV.wav", "length": 69376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_TV.wav", "length": 48384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_MV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_AV.wav", "length": 48576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_PV.wav", "length": 52352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_TV.wav", "length": 52736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_MV.wav", "length": 99968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "72283_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/72283_PV.wav", "length": 84608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "72283_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/72283_TV.wav", "length": 145152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "72283_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/72283_MV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "78582_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/78582_AV.wav", "length": 88768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "78582_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/78582_MV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "81638_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/81638_AV.wav", "length": 82624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "81638_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/81638_MV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_AV.wav", "length": 40384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_PV.wav", "length": 55808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_TV.wav", "length": 60160, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.04 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_MV.wav", "length": 50560, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.64 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84695_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84695_PV.wav", "length": 85184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84695_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84695_MV.wav", "length": 86784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84702_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84702_PV.wav", "length": 59968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84702_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84702_TV.wav", "length": 53952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84702_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84702_MV.wav", "length": 68992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_AV.wav", "length": 63168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_PV.wav", "length": 58368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_TV.wav", "length": 57536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_MV.wav", "length": 60800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_AV.wav", "length": 62336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_PV.wav", "length": 41408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_TV.wav", "length": 63616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_MV.wav", "length": 64768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84713_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84713_AV.wav", "length": 96320, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.08 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84713_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84713_PV.wav", "length": 87552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84713_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84713_TV.wav", "length": 119616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_AV.wav", "length": 62720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_PV.wav", "length": 59456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_TV.wav", "length": 57984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_MV.wav", "length": 60352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84732_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84732_AV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84732_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84732_MV.wav", "length": 79424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_AV.wav", "length": 59584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_PV.wav", "length": 65792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_TV.wav", "length": 56576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_MV.wav", "length": 63168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_AV.wav", "length": 87424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_PV.wav", "length": 88768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_TV.wav", "length": 86592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_MV.wav", "length": 90368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_AV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_PV.wav", "length": 45376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_TV.wav", "length": 41152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_MV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_AV.wav", "length": 83392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_PV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_TV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_MV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84768_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84768_PV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84768_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84768_TV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_AV.wav", "length": 60800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_PV.wav", "length": 68736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_TV.wav", "length": 72384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_MV.wav", "length": 43968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_AV.wav", "length": 63552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_PV.wav", "length": 53760, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.44 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_TV.wav", "length": 50240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_MV.wav", "length": 54208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_AV.wav", "length": 65536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_PV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_TV.wav", "length": 57600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_MV.wav", "length": 71808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_AV.wav", "length": 51136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_PV.wav", "length": 46784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_TV.wav", "length": 51840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_MV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84796_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84796_AV.wav", "length": 92608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84796_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84796_MV.wav", "length": 87424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84807_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84807_AV.wav", "length": 79616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84807_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84807_MV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_AV.wav", "length": 44992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_PV.wav", "length": 54400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_TV.wav", "length": 52416, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_MV.wav", "length": 69568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84824_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84824_AV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84824_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84824_MV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_AV.wav", "length": 75008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_PV.wav", "length": 74752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_TV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_MV.wav", "length": 79936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_AV.wav", "length": 74624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_PV.wav", "length": 78208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_TV.wav", "length": 75008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_MV.wav", "length": 76800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_AV.wav", "length": 79360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_PV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_TV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_MV.wav", "length": 71168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84893_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84893_AV.wav", "length": 89152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84893_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84893_PV.wav", "length": 92352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_AV.wav", "length": 84800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_PV.wav", "length": 82816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_TV.wav", "length": 90624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_MV.wav", "length": 83584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_AV.wav", "length": 30720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_PV.wav", "length": 36800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_TV.wav", "length": 35968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_MV.wav", "length": 81408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84934_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84934_AV.wav", "length": 121536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_AV.wav", "length": 88576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_PV.wav", "length": 145792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_TV.wav", "length": 77952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_MV.wav", "length": 82176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_AV.wav", "length": 84800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_PV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_TV.wav", "length": 42816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_MV.wav", "length": 73408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_AV.wav", "length": 43584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_PV.wav", "length": 43968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_TV.wav", "length": 30784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_MV.wav", "length": 48000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_AV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_PV.wav", "length": 96192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_TV.wav", "length": 127424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_MV.wav", "length": 101952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84969_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84969_MV.wav", "length": 95808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84970_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84970_AV.wav", "length": 91968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84970_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84970_MV.wav", "length": 76736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_AV.wav", "length": 44224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_PV.wav", "length": 40768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_TV.wav", "length": 40768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_MV.wav", "length": 95168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_AV.wav", "length": 83840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_PV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_TV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_MV.wav", "length": 85184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85011_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85011_AV.wav", "length": 82240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85011_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85011_MV.wav", "length": 59968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_AV.wav", "length": 37120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_PV.wav", "length": 32256, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_TV.wav", "length": 37952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_MV.wav", "length": 44736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_AV.wav", "length": 138176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_PV.wav", "length": 86400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_TV.wav", "length": 139584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_MV.wav", "length": 79424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_AV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_PV.wav", "length": 82240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_TV.wav", "length": 80640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_MV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_AV.wav", "length": 48576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_PV.wav", "length": 36992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_TV.wav", "length": 32000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_MV.wav", "length": 92736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_AV.wav", "length": 58624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_PV.wav", "length": 96384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_TV.wav", "length": 46208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_MV.wav", "length": 87936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_AV.wav", "length": 98176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_PV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_TV.wav", "length": 115584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_MV.wav", "length": 64575, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85055_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85055_AV.wav", "length": 99840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85055_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85055_PV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85055_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85055_TV.wav", "length": 108608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_AV.wav", "length": 29568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_PV.wav", "length": 30208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_TV.wav", "length": 45376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_MV.wav", "length": 52608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_AV.wav", "length": 83840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_PV.wav", "length": 86976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_TV.wav", "length": 88768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_MV.wav", "length": 85056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_AV.wav", "length": 90752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_PV.wav", "length": 80960, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.24 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_TV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_MV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_AV.wav", "length": 34176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_PV.wav", "length": 41984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_TV.wav", "length": 31552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_MV.wav", "length": 71424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85090_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85090_PV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85090_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85090_TV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85090_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85090_MV.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_AV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_PV.wav", "length": 95168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_TV.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_MV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85100_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85100_AV.wav", "length": 86272, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.57 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85100_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85100_PV.wav", "length": 91456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_AV.wav", "length": 35968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_PV.wav", "length": 62784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_TV.wav", "length": 63552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_MV.wav", "length": 77568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_AV.wav", "length": 95808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_PV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_TV.wav", "length": 110976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_MV.wav", "length": 82240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_AV.wav", "length": 44736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_PV.wav", "length": 53824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_TV.wav", "length": 61952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_MV.wav", "length": 101824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85140_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85140_AV.wav", "length": 45184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85140_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85140_PV.wav", "length": 63808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85140_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85140_MV.wav", "length": 44608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85150_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85150_AV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85150_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85150_PV.wav", "length": 112256, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_AV.wav", "length": 77824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_PV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_TV.wav", "length": 80576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_MV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_AV.wav", "length": 39552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_PV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_TV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_MV.wav", "length": 115840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85165_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85165_AV.wav", "length": 77952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85165_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85165_MV.wav", "length": 98240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_AV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_PV.wav", "length": 100224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_TV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_MV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_AV.wav", "length": 78592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_TV.wav", "length": 79424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_MV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_AV.wav", "length": 94784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_PV.wav", "length": 100352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_TV.wav", "length": 135168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_MV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_AV.wav", "length": 78336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_PV.wav", "length": 87552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_TV.wav", "length": 83136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_MV.wav", "length": 77184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_AV.wav", "length": 58816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_PV.wav", "length": 64000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_TV.wav", "length": 59008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_MV.wav", "length": 131584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85199_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85199_AV.wav", "length": 93568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85199_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85199_PV.wav", "length": 81792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_AV.wav", "length": 80640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_PV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_TV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_MV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85226_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85226_PV.wav", "length": 68992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85226_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85226_TV.wav", "length": 51776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85226_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85226_MV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_AV.wav", "length": 128000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_PV.wav", "length": 88000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_TV.wav", "length": 69952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_MV.wav", "length": 60992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_AV.wav", "length": 77120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_PV.wav", "length": 81408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_TV.wav", "length": 91968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_MV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85242_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85242_MV.wav", "length": 71936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85243_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85243_AV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85243_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85243_PV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_AV.wav", "length": 79040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_PV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_TV.wav", "length": 80576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_MV.wav", "length": 79040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_AV.wav", "length": 86016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_PV.wav", "length": 99968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_TV.wav", "length": 119424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_MV.wav", "length": 90368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_AV.wav", "length": 91584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_PV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_TV.wav", "length": 76352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_MV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_AV.wav", "length": 72576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_PV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_TV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_MV.wav", "length": 95616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_AV.wav", "length": 75200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_PV.wav", "length": 108736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_TV.wav", "length": 71936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_MV.wav", "length": 134784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85264_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85264_MV.wav", "length": 93184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_AV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_PV.wav", "length": 58368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_TV.wav", "length": 71552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_MV.wav", "length": 101120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_AV.wav", "length": 89792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_PV.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_TV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_MV.wav", "length": 97920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_AV.wav", "length": 80640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_PV.wav", "length": 90624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_TV.wav", "length": 64000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_MV.wav", "length": 79616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_AV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_PV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_TV.wav", "length": 129984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_MV.wav", "length": 86208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85306_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85306_AV.wav", "length": 85056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85306_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85306_MV.wav", "length": 81792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_AV.wav", "length": 79360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_PV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_TV.wav", "length": 85120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_MV.wav", "length": 92224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85313_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85313_PV.wav", "length": 86784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85313_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85313_TV.wav", "length": 108416, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85313_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85313_MV.wav", "length": 76608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85321_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85321_PV.wav", "length": 91776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85321_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85321_TV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85322_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85322_TV.wav", "length": 44800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_AV.wav", "length": 92608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_TV.wav", "length": 83008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_MV.wav", "length": 77568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85339_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85339_MV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_AV.wav", "length": 77056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_PV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_MV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_AV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_PV.wav", "length": 81984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_TV.wav", "length": 78656, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_MV.wav", "length": 79936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_AV.wav", "length": 74816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_PV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
+{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_MV.wav", "length": 78592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5541_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5541_test_sub_8.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9179_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9179_test_sub_12.npy", "length": 108, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_677_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_677_test_sub_6.npy", "length": 237, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7402_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7402_test_sub_15.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1923_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1923_test_sub_17.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8072_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8072_test_sub_0.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8557_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8557_test_sub_2.npy", "length": 261, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_134_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_134_test_sub_18.npy", "length": 82, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6201_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6201_test_sub_1.npy", "length": 228, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_580_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_580_test_sub_15.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9067_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9067_test_sub_4.npy", "length": 179, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1199_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1199_test_sub_5.npy", "length": 261, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6155_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6155_test_sub_6.npy", "length": 207, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2272_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2272_test_sub_4.npy", "length": 102, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2817_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2817_test_sub_12.npy", "length": 144, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8733_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8733_test_sub_9.npy", "length": 301, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4584_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4584_test_sub_17.npy", "length": 90, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7911_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7911_test_sub_6.npy", "length": 141, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8426_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8426_test_sub_13.npy", "length": 116, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5137_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5137_test_sub_16.npy", "length": 143, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8577_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8577_test_sub_11.npy", "length": 173, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8543_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8543_test_sub_13.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4228_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4228_test_sub_8.npy", "length": 82, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4816_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4816_test_sub_6.npy", "length": 113, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5004_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5004_test_sub_7.npy", "length": 187, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9473_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9473_test_sub_7.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7473_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7473_test_sub_18.npy", "length": 42, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4323_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4323_test_sub_0.npy", "length": 33, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2112_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2112_test_sub_2.npy", "length": 110, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3586_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3586_test_sub_3.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2056_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2056_test_sub_14.npy", "length": 195, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4127_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4127_test_sub_1.npy", "length": 258, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2224_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2224_test_sub_2.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4240_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4240_test_sub_1.npy", "length": 49, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3939_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3939_test_sub_0.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5736_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5736_test_sub_6.npy", "length": 142, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1709_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1709_test_sub_2.npy", "length": 200, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3862_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3862_test_sub_5.npy", "length": 250, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4600_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4600_test_sub_11.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6556_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6556_test_sub_0.npy", "length": 307, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7487_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7487_test_sub_9.npy", "length": 56, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5274_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5274_test_sub_8.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_405_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_405_test_sub_3.npy", "length": 190, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_915_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_915_test_sub_0.npy", "length": 236, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9445_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9445_test_sub_2.npy", "length": 275, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9736_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9736_test_sub_10.npy", "length": 105, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1647_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1647_test_sub_2.npy", "length": 73, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9604_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9604_test_sub_11.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6462_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6462_test_sub_2.npy", "length": 33, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5897_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5897_test_sub_4.npy", "length": 250, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5981_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5981_test_sub_5.npy", "length": 67, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1778_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1778_test_sub_11.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4544_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4544_test_sub_2.npy", "length": 238, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7523_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7523_test_sub_2.npy", "length": 40, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2330_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2330_test_sub_12.npy", "length": 246, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7511_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7511_test_sub_12.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4296_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4296_test_sub_10.npy", "length": 37, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1588_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1588_test_sub_15.npy", "length": 121, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2918_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2918_test_sub_17.npy", "length": 141, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6464_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6464_test_sub_0.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_76_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_76_test_sub_2.npy", "length": 308, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_352_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_352_test_sub_11.npy", "length": 146, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9926_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9926_test_sub_18.npy", "length": 178, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1400_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1400_test_sub_18.npy", "length": 293, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8489_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8489_test_sub_3.npy", "length": 243, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1506_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1506_test_sub_4.npy", "length": 168, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7350_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7350_test_sub_20.npy", "length": 214, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9227_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9227_test_sub_2.npy", "length": 92, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9230_test_sub_23", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9230_test_sub_23.npy", "length": 204, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9567_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9567_test_sub_0.npy", "length": 212, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5533_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5533_test_sub_2.npy", "length": 71, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5730_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5730_test_sub_11.npy", "length": 52, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3329_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3329_test_sub_0.npy", "length": 205, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1098_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1098_test_sub_6.npy", "length": 216, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5793_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5793_test_sub_1.npy", "length": 78, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3853_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3853_test_sub_13.npy", "length": 195, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9360_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9360_test_sub_3.npy", "length": 190, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9169_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9169_test_sub_4.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3553_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3553_test_sub_3.npy", "length": 284, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1694_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1694_test_sub_4.npy", "length": 76, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_486_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_486_test_sub_4.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9980_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9980_test_sub_4.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2548_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2548_test_sub_5.npy", "length": 66, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7703_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7703_test_sub_3.npy", "length": 64, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3073_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3073_test_sub_4.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2070_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2070_test_sub_11.npy", "length": 57, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8364_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8364_test_sub_10.npy", "length": 222, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8324_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8324_test_sub_7.npy", "length": 155, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8040_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8040_test_sub_3.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5967_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5967_test_sub_5.npy", "length": 226, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4100_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4100_test_sub_4.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4231_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4231_test_sub_14.npy", "length": 170, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4214_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4214_test_sub_2.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7314_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7314_test_sub_0.npy", "length": 54, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1253_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1253_test_sub_7.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4421_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4421_test_sub_5.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5783_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5783_test_sub_9.npy", "length": 227, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3073_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3073_test_sub_14.npy", "length": 174, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_841_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_841_test_sub_12.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7577_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7577_test_sub_5.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3233_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3233_test_sub_4.npy", "length": 101, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4803_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4803_test_sub_0.npy", "length": 91, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2273_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2273_test_sub_7.npy", "length": 107, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3285_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3285_test_sub_12.npy", "length": 118, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4065_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4065_test_sub_1.npy", "length": 74, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4535_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4535_test_sub_2.npy", "length": 100, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_127_test_sub_24", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_127_test_sub_24.npy", "length": 143, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8198_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8198_test_sub_0.npy", "length": 49, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7550_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7550_test_sub_17.npy", "length": 64, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3356_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3356_test_sub_4.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6441_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6441_test_sub_10.npy", "length": 167, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9742_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9742_test_sub_12.npy", "length": 297, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2159_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2159_test_sub_3.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8788_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8788_test_sub_1.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9842_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9842_test_sub_0.npy", "length": 264, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6562_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6562_test_sub_11.npy", "length": 136, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2642_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2642_test_sub_2.npy", "length": 65, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1970_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1970_test_sub_2.npy", "length": 65, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4979_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4979_test_sub_0.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6209_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6209_test_sub_7.npy", "length": 63, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9967_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9967_test_sub_8.npy", "length": 260, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5520_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5520_test_sub_3.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3952_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3952_test_sub_3.npy", "length": 261, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5701_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5701_test_sub_12.npy", "length": 270, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1160_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1160_test_sub_5.npy", "length": 188, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5211_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5211_test_sub_0.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4765_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4765_test_sub_7.npy", "length": 47, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4676_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4676_test_sub_11.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_112_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_112_test_sub_9.npy", "length": 150, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7432_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7432_test_sub_12.npy", "length": 120, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8461_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8461_test_sub_9.npy", "length": 139, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4744_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4744_test_sub_15.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6667_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6667_test_sub_9.npy", "length": 297, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6857_test_sub_23", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6857_test_sub_23.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8449_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8449_test_sub_10.npy", "length": 252, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9936_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9936_test_sub_18.npy", "length": 220, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1188_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1188_test_sub_20.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5386_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5386_test_sub_8.npy", "length": 240, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1207_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1207_test_sub_1.npy", "length": 241, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2506_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2506_test_sub_8.npy", "length": 52, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9012_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9012_test_sub_18.npy", "length": 75, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8034_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8034_test_sub_5.npy", "length": 255, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9296_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9296_test_sub_2.npy", "length": 66, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2514_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2514_test_sub_13.npy", "length": 100, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1269_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1269_test_sub_15.npy", "length": 310, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9446_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9446_test_sub_1.npy", "length": 312, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2356_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2356_test_sub_5.npy", "length": 51, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3122_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3122_test_sub_3.npy", "length": 276, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1392_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1392_test_sub_5.npy", "length": 277, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7514_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7514_test_sub_0.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1848_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1848_test_sub_3.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9777_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9777_test_sub_0.npy", "length": 302, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7456_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7456_test_sub_8.npy", "length": 44, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_319_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_319_test_sub_7.npy", "length": 284, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2038_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2038_test_sub_10.npy", "length": 106, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3188_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3188_test_sub_5.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1981_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1981_test_sub_2.npy", "length": 57, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7344_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7344_test_sub_8.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9896_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9896_test_sub_9.npy", "length": 189, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7101_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7101_test_sub_10.npy", "length": 193, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5790_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5790_test_sub_17.npy", "length": 67, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2626_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2626_test_sub_8.npy", "length": 242, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6_test_sub_15.npy", "length": 47, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6240_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6240_test_sub_11.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6300_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6300_test_sub_5.npy", "length": 138, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_376_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_376_test_sub_16.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6205_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6205_test_sub_3.npy", "length": 288, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2030_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2030_test_sub_10.npy", "length": 220, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1499_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1499_test_sub_3.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2091_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2091_test_sub_0.npy", "length": 90, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3744_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3744_test_sub_1.npy", "length": 55, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_923_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_923_test_sub_7.npy", "length": 45, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_829_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_829_test_sub_12.npy", "length": 44, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9320_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9320_test_sub_11.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6069_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6069_test_sub_3.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_619_test_sub_25", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_619_test_sub_25.npy", "length": 81, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7412_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7412_test_sub_17.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7677_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7677_test_sub_1.npy", "length": 118, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1395_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1395_test_sub_3.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1730_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1730_test_sub_10.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5010_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5010_test_sub_7.npy", "length": 251, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1384_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1384_test_sub_3.npy", "length": 314, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7702_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7702_test_sub_0.npy", "length": 147, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5096_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5096_test_sub_15.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8183_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8183_test_sub_6.npy", "length": 73, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6420_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6420_test_sub_2.npy", "length": 110, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2070_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2070_test_sub_8.npy", "length": 207, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5693_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5693_test_sub_12.npy", "length": 231, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6633_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6633_test_sub_8.npy", "length": 274, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6006_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6006_test_sub_10.npy", "length": 317, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9229_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9229_test_sub_0.npy", "length": 106, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7426_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7426_test_sub_7.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2823_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2823_test_sub_13.npy", "length": 183, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_750_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_750_test_sub_3.npy", "length": 318, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5602_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5602_test_sub_11.npy", "length": 103, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5791_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5791_test_sub_8.npy", "length": 290, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8543_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8543_test_sub_10.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9284_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9284_test_sub_0.npy", "length": 244, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9319_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9319_test_sub_16.npy", "length": 99, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4412_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4412_test_sub_1.npy", "length": 117, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9541_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9541_test_sub_13.npy", "length": 67, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6552_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6552_test_sub_8.npy", "length": 238, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6410_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6410_test_sub_0.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5056_test_sub_19", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5056_test_sub_19.npy", "length": 109, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1342_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1342_test_sub_6.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1248_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1248_test_sub_7.npy", "length": 273, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_512_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_512_test_sub_17.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3424_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3424_test_sub_5.npy", "length": 236, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1130_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1130_test_sub_2.npy", "length": 170, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2651_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2651_test_sub_11.npy", "length": 81, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8508_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8508_test_sub_5.npy", "length": 86, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8911_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8911_test_sub_7.npy", "length": 221, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8543_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8543_test_sub_16.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_900_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_900_test_sub_7.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1969_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1969_test_sub_17.npy", "length": 251, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9802_test_sub_24", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9802_test_sub_24.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4380_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4380_test_sub_16.npy", "length": 127, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7472_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7472_test_sub_11.npy", "length": 84, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4222_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4222_test_sub_12.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7602_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7602_test_sub_0.npy", "length": 277, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9049_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9049_test_sub_2.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2783_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2783_test_sub_14.npy", "length": 235, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8351_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8351_test_sub_14.npy", "length": 88, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6940_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6940_test_sub_17.npy", "length": 142, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2952_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2952_test_sub_14.npy", "length": 274, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8695_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8695_test_sub_11.npy", "length": 213, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9306_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9306_test_sub_7.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2760_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2760_test_sub_7.npy", "length": 216, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5387_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5387_test_sub_0.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3960_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3960_test_sub_20.npy", "length": 160, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9803_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9803_test_sub_6.npy", "length": 297, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1314_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1314_test_sub_5.npy", "length": 218, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1601_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1601_test_sub_2.npy", "length": 169, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5473_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5473_test_sub_1.npy", "length": 299, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2611_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2611_test_sub_14.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9466_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9466_test_sub_6.npy", "length": 146, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1243_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1243_test_sub_3.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8816_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8816_test_sub_3.npy", "length": 218, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5062_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5062_test_sub_14.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2183_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2183_test_sub_9.npy", "length": 249, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3179_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3179_test_sub_9.npy", "length": 237, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4501_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4501_test_sub_1.npy", "length": 270, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4072_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4072_test_sub_4.npy", "length": 267, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1753_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1753_test_sub_3.npy", "length": 318, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2826_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2826_test_sub_2.npy", "length": 129, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1393_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1393_test_sub_7.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8248_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8248_test_sub_11.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_552_test_sub_29", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_552_test_sub_29.npy", "length": 293, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6054_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6054_test_sub_6.npy", "length": 184, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3312_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3312_test_sub_16.npy", "length": 260, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_938_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_938_test_sub_5.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2369_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2369_test_sub_8.npy", "length": 228, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2046_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2046_test_sub_10.npy", "length": 315, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8985_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8985_test_sub_2.npy", "length": 179, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5671_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5671_test_sub_9.npy", "length": 41, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4219_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4219_test_sub_1.npy", "length": 311, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2343_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2343_test_sub_2.npy", "length": 184, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3543_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3543_test_sub_3.npy", "length": 127, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_311_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_311_test_sub_4.npy", "length": 185, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9792_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9792_test_sub_3.npy", "length": 300, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2273_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2273_test_sub_11.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6154_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6154_test_sub_3.npy", "length": 232, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6594_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6594_test_sub_2.npy", "length": 258, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1471_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1471_test_sub_3.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3115_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3115_test_sub_7.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_831_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_831_test_sub_2.npy", "length": 114, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1571_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1571_test_sub_8.npy", "length": 268, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6551_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6551_test_sub_2.npy", "length": 36, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5687_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5687_test_sub_2.npy", "length": 215, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1287_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1287_test_sub_11.npy", "length": 313, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4883_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4883_test_sub_7.npy", "length": 184, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_20_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_20_test_sub_2.npy", "length": 144, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7269_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7269_test_sub_5.npy", "length": 279, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1546_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1546_test_sub_10.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4700_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4700_test_sub_6.npy", "length": 251, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9597_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9597_test_sub_1.npy", "length": 273, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8170_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8170_test_sub_12.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1637_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1637_test_sub_7.npy", "length": 146, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8280_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8280_test_sub_8.npy", "length": 231, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4043_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4043_test_sub_11.npy", "length": 300, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9985_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9985_test_sub_1.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5438_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5438_test_sub_17.npy", "length": 143, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3029_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3029_test_sub_9.npy", "length": 216, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4634_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4634_test_sub_0.npy", "length": 69, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2670_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2670_test_sub_2.npy", "length": 49, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6397_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6397_test_sub_0.npy", "length": 117, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_956_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_956_test_sub_13.npy", "length": 179, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4742_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4742_test_sub_2.npy", "length": 98, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5317_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5317_test_sub_8.npy", "length": 126, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8564_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8564_test_sub_5.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3166_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3166_test_sub_3.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9285_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9285_test_sub_6.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3197_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3197_test_sub_12.npy", "length": 167, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1265_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1265_test_sub_2.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7395_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7395_test_sub_0.npy", "length": 241, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1070_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1070_test_sub_0.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_803_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_803_test_sub_2.npy", "length": 98, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1506_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1506_test_sub_3.npy", "length": 264, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9690_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9690_test_sub_12.npy", "length": 235, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_106_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_106_test_sub_5.npy", "length": 270, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7068_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7068_test_sub_9.npy", "length": 188, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3613_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3613_test_sub_10.npy", "length": 205, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9027_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9027_test_sub_7.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1214_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1214_test_sub_6.npy", "length": 139, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3740_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3740_test_sub_3.npy", "length": 134, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_993_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_993_test_sub_10.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3287_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3287_test_sub_7.npy", "length": 203, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5470_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5470_test_sub_10.npy", "length": 134, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5367_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5367_test_sub_0.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5303_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5303_test_sub_14.npy", "length": 120, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9654_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9654_test_sub_5.npy", "length": 78, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3831_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3831_test_sub_5.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_296_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_296_test_sub_9.npy", "length": 192, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2844_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2844_test_sub_1.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4302_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4302_test_sub_5.npy", "length": 319, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_386_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_386_test_sub_5.npy", "length": 254, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1240_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1240_test_sub_12.npy", "length": 41, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9130_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9130_test_sub_0.npy", "length": 224, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_289_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_289_test_sub_0.npy", "length": 228, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8717_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8717_test_sub_16.npy", "length": 311, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8617_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8617_test_sub_14.npy", "length": 209, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6869_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6869_test_sub_0.npy", "length": 244, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4264_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4264_test_sub_0.npy", "length": 34, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_367_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_367_test_sub_20.npy", "length": 295, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3275_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3275_test_sub_2.npy", "length": 281, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5802_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5802_test_sub_10.npy", "length": 166, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_405_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_405_test_sub_8.npy", "length": 38, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_603_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_603_test_sub_0.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1008_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1008_test_sub_4.npy", "length": 253, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2273_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2273_test_sub_17.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_526_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_526_test_sub_9.npy", "length": 301, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6780_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6780_test_sub_13.npy", "length": 192, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6872_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6872_test_sub_16.npy", "length": 38, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1974_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1974_test_sub_14.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5127_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5127_test_sub_0.npy", "length": 291, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7466_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7466_test_sub_8.npy", "length": 131, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5654_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5654_test_sub_16.npy", "length": 114, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3000_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3000_test_sub_18.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4382_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4382_test_sub_0.npy", "length": 96, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9335_test_sub_25", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9335_test_sub_25.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_888_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_888_test_sub_9.npy", "length": 299, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6236_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6236_test_sub_5.npy", "length": 277, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9371_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9371_test_sub_3.npy", "length": 285, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9194_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9194_test_sub_0.npy", "length": 225, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8021_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8021_test_sub_5.npy", "length": 263, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7213_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7213_test_sub_13.npy", "length": 310, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7333_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7333_test_sub_0.npy", "length": 246, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2611_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2611_test_sub_6.npy", "length": 83, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2172_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2172_test_sub_8.npy", "length": 34, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_838_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_838_test_sub_11.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8165_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8165_test_sub_2.npy", "length": 313, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4786_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4786_test_sub_13.npy", "length": 226, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4134_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4134_test_sub_11.npy", "length": 46, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6433_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6433_test_sub_12.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5785_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5785_test_sub_1.npy", "length": 95, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3272_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3272_test_sub_11.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2477_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2477_test_sub_0.npy", "length": 114, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2161_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2161_test_sub_11.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1437_test_sub_26", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1437_test_sub_26.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1555_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1555_test_sub_18.npy", "length": 243, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8208_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8208_test_sub_9.npy", "length": 158, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2070_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2070_test_sub_0.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8364_test_sub_21", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8364_test_sub_21.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_114_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_114_test_sub_20.npy", "length": 234, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7891_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7891_test_sub_10.npy", "length": 96, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9320_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9320_test_sub_16.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1909_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1909_test_sub_11.npy", "length": 178, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8163_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8163_test_sub_4.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_365_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_365_test_sub_9.npy", "length": 96, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7269_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7269_test_sub_2.npy", "length": 212, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2242_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2242_test_sub_7.npy", "length": 107, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3050_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3050_test_sub_6.npy", "length": 139, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4207_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4207_test_sub_0.npy", "length": 287, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3914_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3914_test_sub_1.npy", "length": 240, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7298_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7298_test_sub_6.npy", "length": 316, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2230_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2230_test_sub_1.npy", "length": 185, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7818_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7818_test_sub_3.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5789_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5789_test_sub_0.npy", "length": 256, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_28_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_28_test_sub_4.npy", "length": 138, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_539_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_539_test_sub_0.npy", "length": 128, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_787_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_787_test_sub_10.npy", "length": 280, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2224_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2224_test_sub_11.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8790_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8790_test_sub_5.npy", "length": 268, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9466_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9466_test_sub_5.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5791_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5791_test_sub_3.npy", "length": 170, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_274_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_274_test_sub_1.npy", "length": 214, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5423_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5423_test_sub_14.npy", "length": 233, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4040_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4040_test_sub_3.npy", "length": 290, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 0}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8191_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8191_test_sub_7.npy", "length": 104, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1324_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1324_test_sub_5.npy", "length": 304, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9594_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9594_test_sub_12.npy", "length": 83, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5393_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5393_test_sub_4.npy", "length": 305, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5313_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5313_test_sub_6.npy", "length": 187, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9179_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9179_test_sub_13.npy", "length": 288, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1736_test_sub_36", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1736_test_sub_36.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1114_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1114_test_sub_9.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8685_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8685_test_sub_3.npy", "length": 103, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_188_test_sub_22", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_188_test_sub_22.npy", "length": 36, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5404_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5404_test_sub_8.npy", "length": 192, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2712_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2712_test_sub_3.npy", "length": 119, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6306_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6306_test_sub_6.npy", "length": 309, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4460_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4460_test_sub_2.npy", "length": 227, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8455_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8455_test_sub_12.npy", "length": 298, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5702_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5702_test_sub_3.npy", "length": 126, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8003_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8003_test_sub_8.npy", "length": 298, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -2}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7484_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7484_test_sub_6.npy", "length": 83, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3095_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3095_test_sub_0.npy", "length": 70, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3083_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3083_test_sub_12.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9073_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9073_test_sub_1.npy", "length": 36, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3173_test_sub_32", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3173_test_sub_32.npy", "length": 271, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4986_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4986_test_sub_3.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3441_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3441_test_sub_1.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2213_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2213_test_sub_3.npy", "length": 104, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1762_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1762_test_sub_4.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3638_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3638_test_sub_2.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -6}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8276_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8276_test_sub_1.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5780_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5780_test_sub_9.npy", "length": 61, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6363_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6363_test_sub_0.npy", "length": 157, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3762_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3762_test_sub_5.npy", "length": 289, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8272_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8272_test_sub_6.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3503_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3503_test_sub_5.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8639_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8639_test_sub_5.npy", "length": 86, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1026_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1026_test_sub_15.npy", "length": 189, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5021_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5021_test_sub_3.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8578_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8578_test_sub_8.npy", "length": 312, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8355_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8355_test_sub_8.npy", "length": 168, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2890_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2890_test_sub_6.npy", "length": 97, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6302_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6302_test_sub_10.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5200_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5200_test_sub_11.npy", "length": 195, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6791_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6791_test_sub_7.npy", "length": 145, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4082_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4082_test_sub_12.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4312_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4312_test_sub_10.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6931_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6931_test_sub_8.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8315_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8315_test_sub_12.npy", "length": 255, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_915_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_915_test_sub_6.npy", "length": 254, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8676_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8676_test_sub_1.npy", "length": 272, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5209_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5209_test_sub_14.npy", "length": 160, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4829_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4829_test_sub_2.npy", "length": 272, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3890_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3890_test_sub_7.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2261_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2261_test_sub_15.npy", "length": 206, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -19}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1778_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1778_test_sub_8.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_826_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_826_test_sub_9.npy", "length": 288, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3181_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3181_test_sub_3.npy", "length": 314, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1462_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1462_test_sub_5.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2233_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2233_test_sub_3.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_630_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_630_test_sub_7.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6536_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6536_test_sub_6.npy", "length": 45, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8475_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8475_test_sub_1.npy", "length": 279, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4925_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4925_test_sub_10.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9075_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9075_test_sub_8.npy", "length": 72, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4624_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4624_test_sub_12.npy", "length": 121, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2089_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2089_test_sub_7.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9189_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9189_test_sub_14.npy", "length": 237, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4445_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4445_test_sub_1.npy", "length": 186, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5963_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5963_test_sub_7.npy", "length": 234, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_162_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_162_test_sub_10.npy", "length": 236, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6114_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6114_test_sub_3.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4172_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4172_test_sub_20.npy", "length": 117, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1425_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1425_test_sub_6.npy", "length": 282, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8688_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8688_test_sub_4.npy", "length": 209, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7191_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7191_test_sub_8.npy", "length": 63, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7627_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7627_test_sub_0.npy", "length": 168, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -18}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6680_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6680_test_sub_14.npy", "length": 157, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2039_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2039_test_sub_1.npy", "length": 95, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3637_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3637_test_sub_3.npy", "length": 205, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2084_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2084_test_sub_5.npy", "length": 254, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5235_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5235_test_sub_0.npy", "length": 86, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8205_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8205_test_sub_7.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2723_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2723_test_sub_3.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4817_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4817_test_sub_3.npy", "length": 88, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -7}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4879_test_sub_24", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4879_test_sub_24.npy", "length": 47, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -10}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1954_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1954_test_sub_0.npy", "length": 212, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -16}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4449_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4449_test_sub_7.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3191_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3191_test_sub_12.npy", "length": 234, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -8}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6608_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6608_test_sub_18.npy", "length": 307, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 17}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3192_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3192_test_sub_0.npy", "length": 202, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7754_test_sub_19", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7754_test_sub_19.npy", "length": 40, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1551_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1551_test_sub_7.npy", "length": 142, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9755_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9755_test_sub_0.npy", "length": 159, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 1}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3954_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3954_test_sub_12.npy", "length": 289, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 20}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2848_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2848_test_sub_18.npy", "length": 166, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
+{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8831_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8831_test_sub_15.npy", "length": 169, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 16}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000143", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000143.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 143}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000156", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000156.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 156}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-4_000034", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000034.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -4, "sample_index_in_group": 34}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-4_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -4, "sample_index_in_group": 133}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-10_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-10_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -10, "sample_index_in_group": 140}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-2_000063", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-2_000063.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -2, "sample_index_in_group": 63}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 33}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 79}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_4_000144", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_4_000144.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 4, "sample_index_in_group": 144}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 108}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000127", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000127.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 127}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_0_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_0_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 0, "sample_index_in_group": 196}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-8_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-8_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -8, "sample_index_in_group": 76}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 171}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-20_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-20_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -20, "sample_index_in_group": 2}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-12_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-12_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -12, "sample_index_in_group": 33}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-20_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-20_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -20, "sample_index_in_group": 131}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_10_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_10_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 10, "sample_index_in_group": 166}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_12_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_12_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 12, "sample_index_in_group": 197}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_18_000016", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_18_000016.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 18, "sample_index_in_group": 16}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-20_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-20_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -20, "sample_index_in_group": 128}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-6_000163", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-6_000163.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -6, "sample_index_in_group": 163}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 26}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_0_000080", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_0_000080.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 0, "sample_index_in_group": 80}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-6_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-6_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -6, "sample_index_in_group": 199}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 32}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000174", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000174.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 174}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-20_000038", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-20_000038.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -20, "sample_index_in_group": 38}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-2_000117", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-2_000117.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -2, "sample_index_in_group": 117}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 71}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-2_000039", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-2_000039.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -2, "sample_index_in_group": 39}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_4_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_4_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 4, "sample_index_in_group": 77}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000090", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000090.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 90}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 29}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000039", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000039.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 39}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_10_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_10_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 10, "sample_index_in_group": 13}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 61}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-6_000176", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-6_000176.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -6, "sample_index_in_group": 176}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_4_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_4_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 4, "sample_index_in_group": 37}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-4_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-4_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -4, "sample_index_in_group": 128}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_4_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_4_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 4, "sample_index_in_group": 95}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_12_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_12_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 12, "sample_index_in_group": 13}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 152}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000074", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000074.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 74}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_0_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_0_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 0, "sample_index_in_group": 21}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000151", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000151.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 151}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-4_000132", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-4_000132.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -4, "sample_index_in_group": 132}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-16_000125", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-16_000125.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -16, "sample_index_in_group": 125}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_6_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_6_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 6, "sample_index_in_group": 102}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000172", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000172.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 172}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_18_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_18_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 18, "sample_index_in_group": 120}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_8_000085", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_8_000085.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 8, "sample_index_in_group": 85}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000016", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000016.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 16}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000145", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000145.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 145}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000122", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000122.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 122}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_4_000174", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_4_000174.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 4, "sample_index_in_group": 174}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-20_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-20_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -20, "sample_index_in_group": 86}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000054", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000054.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 54}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-14_000046", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-14_000046.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -14, "sample_index_in_group": 46}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000016", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000016.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 16}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-6_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-6_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -6, "sample_index_in_group": 131}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 8}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 194}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 26}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_14_000127", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_14_000127.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 14, "sample_index_in_group": 127}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-4_000043", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-4_000043.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -4, "sample_index_in_group": 43}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000056", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000056.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 56}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_18_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_18_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 18, "sample_index_in_group": 36}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 129}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 92}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_2_000066", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_2_000066.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 2, "sample_index_in_group": 66}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-2_000057", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-2_000057.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -2, "sample_index_in_group": 57}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-12_000091", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-12_000091.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -12, "sample_index_in_group": 91}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 189}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_4_000042", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_4_000042.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 4, "sample_index_in_group": 42}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-8_000153", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-8_000153.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -8, "sample_index_in_group": 153}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_12_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_12_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 12, "sample_index_in_group": 79}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 199}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-20_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-20_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -20, "sample_index_in_group": 79}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_18_000045", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_18_000045.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 18, "sample_index_in_group": 45}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_18_000169", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_18_000169.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 18, "sample_index_in_group": 169}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_4_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_4_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 4, "sample_index_in_group": 18}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 192}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_2_000123", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_2_000123.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 2, "sample_index_in_group": 123}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 21}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_4_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_4_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 4, "sample_index_in_group": 13}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_4_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_4_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 4, "sample_index_in_group": 72}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000068", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000068.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 68}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000148", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000148.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 148}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-8_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-8_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -8, "sample_index_in_group": 86}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000141", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000141.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 141}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-8_000193", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-8_000193.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -8, "sample_index_in_group": 193}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_16_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_16_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 16, "sample_index_in_group": 177}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000195", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000195.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 195}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_2_000051", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_2_000051.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 2, "sample_index_in_group": 51}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000024", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000024.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 24}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_4_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_4_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 4, "sample_index_in_group": 133}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 83}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_10_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_10_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 10, "sample_index_in_group": 79}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_8_000162", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_8_000162.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 8, "sample_index_in_group": 162}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000106", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000106.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 106}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_16_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_16_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 16, "sample_index_in_group": 194}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_6_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_6_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 6, "sample_index_in_group": 61}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000048", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000048.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 48}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 33}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000167", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000167.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 167}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_0_000167", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_0_000167.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 0, "sample_index_in_group": 167}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_4_000094", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_4_000094.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 4, "sample_index_in_group": 94}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 99}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 133}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_4_000010", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_4_000010.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 4, "sample_index_in_group": 10}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_12_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_12_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 12, "sample_index_in_group": 105}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 49}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-18_000097", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-18_000097.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -18, "sample_index_in_group": 97}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_0_000141", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_0_000141.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 0, "sample_index_in_group": 141}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000009", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000009.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 9}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000188", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000188.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 188}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 108}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000179", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000179.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 179}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_4_000170", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_4_000170.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 4, "sample_index_in_group": 170}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_18_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_18_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 18, "sample_index_in_group": 177}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 108}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_16_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_16_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 16, "sample_index_in_group": 14}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-18_000100", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-18_000100.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -18, "sample_index_in_group": 100}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-16_000056", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-16_000056.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -16, "sample_index_in_group": 56}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-18_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-18_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -18, "sample_index_in_group": 29}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 120}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-8_000088", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-8_000088.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -8, "sample_index_in_group": 88}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000024", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000024.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 24}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-8_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-8_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -8, "sample_index_in_group": 59}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-14_000179", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-14_000179.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -14, "sample_index_in_group": 179}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-14_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-14_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -14, "sample_index_in_group": 76}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 1}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 37}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000005", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000005.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 5}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 76}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_14_000130", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_14_000130.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 14, "sample_index_in_group": 130}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-16_000058", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-16_000058.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -16, "sample_index_in_group": 58}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_12_000146", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_12_000146.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 12, "sample_index_in_group": 146}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_16_000046", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_16_000046.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 16, "sample_index_in_group": 46}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000181", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000181.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 181}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-12_000062", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-12_000062.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -12, "sample_index_in_group": 62}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000020", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000020.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 20}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_14_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_14_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 14, "sample_index_in_group": 92}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_6_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_6_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 6, "sample_index_in_group": 78}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-20_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-20_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -20, "sample_index_in_group": 187}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 138}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 115}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-12_000075", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-12_000075.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -12, "sample_index_in_group": 75}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000004", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000004.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 4}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000064", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000064.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 64}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_14_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_14_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 14, "sample_index_in_group": 196}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 8}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_0_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_0_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 0, "sample_index_in_group": 187}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-10_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-10_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -10, "sample_index_in_group": 78}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 105}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-10_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-10_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -10, "sample_index_in_group": 138}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-14_000100", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-14_000100.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -14, "sample_index_in_group": 100}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_0_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_0_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 0, "sample_index_in_group": 72}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-8_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-8_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -8, "sample_index_in_group": 189}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-10_000023", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-10_000023.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -10, "sample_index_in_group": 23}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_18_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_18_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 18, "sample_index_in_group": 150}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000126", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000126.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 126}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-6_000004", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-6_000004.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -6, "sample_index_in_group": 4}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 138}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 47}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_2_000025", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_2_000025.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 2, "sample_index_in_group": 25}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_6_000151", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_6_000151.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 6, "sample_index_in_group": 151}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000158", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000158.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 158}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 8}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000103", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000103.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 103}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 165}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-4_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-4_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -4, "sample_index_in_group": 194}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_2_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_2_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 2, "sample_index_in_group": 32}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 1}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 49}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-10_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-10_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -10, "sample_index_in_group": 15}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-14_000055", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-14_000055.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -14, "sample_index_in_group": 55}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 14}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 136}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-2_000193", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-2_000193.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -2, "sample_index_in_group": 193}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 165}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-2_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-2_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -2, "sample_index_in_group": 72}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000091", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000091.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 91}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-8_000178", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-8_000178.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -8, "sample_index_in_group": 178}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-10_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-10_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -10, "sample_index_in_group": 140}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-10_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-10_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -10, "sample_index_in_group": 18}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000094", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000094.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 94}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-20_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-20_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -20, "sample_index_in_group": 192}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-4_000020", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-4_000020.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -4, "sample_index_in_group": 20}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000193", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000193.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 193}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000114", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000114.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 114}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000023", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000023.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 23}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_8_000132", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_8_000132.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 8, "sample_index_in_group": 132}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-14_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-14_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -14, "sample_index_in_group": 187}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000062", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000062.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 62}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-20_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-20_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -20, "sample_index_in_group": 13}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-8_000039", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-8_000039.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -8, "sample_index_in_group": 39}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000160", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000160.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 160}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-10_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-10_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -10, "sample_index_in_group": 92}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-12_000168", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-12_000168.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -12, "sample_index_in_group": 168}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-4_000117", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-4_000117.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -4, "sample_index_in_group": 117}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000028", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000028.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 28}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000182", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000182.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 182}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-2_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-2_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -2, "sample_index_in_group": 190}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-8_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-8_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -8, "sample_index_in_group": 95}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 21}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-4_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-4_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -4, "sample_index_in_group": 53}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000070", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000070.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 70}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_8_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_8_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 8, "sample_index_in_group": 72}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_4_000009", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_4_000009.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 4, "sample_index_in_group": 9}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_2_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_2_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 2, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-4_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -4, "sample_index_in_group": 194}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-20_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-20_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -20, "sample_index_in_group": 177}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 102}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 76}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000003", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000003.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 3}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000034", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000034.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 34}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-16_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-16_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -16, "sample_index_in_group": 18}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_6_000111", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_6_000111.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 6, "sample_index_in_group": 111}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-16_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-16_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -16, "sample_index_in_group": 171}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_18_000060", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_18_000060.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 18, "sample_index_in_group": 60}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_2_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_2_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 2, "sample_index_in_group": 8}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000005", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000005.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 5}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_10_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_10_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 10, "sample_index_in_group": 136}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000130", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000130.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 130}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_0_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_0_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 0, "sample_index_in_group": 166}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 8}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000042", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000042.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 42}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_8_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_8_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 8, "sample_index_in_group": 192}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 140}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000066", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000066.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 66}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000126", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000126.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 126}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_8_000139", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_8_000139.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 8, "sample_index_in_group": 139}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 95}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_8_000157", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_8_000157.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 8, "sample_index_in_group": 157}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_0_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_0_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 0, "sample_index_in_group": 196}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_10_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_10_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 10, "sample_index_in_group": 86}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 120}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_14_000125", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_14_000125.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 14, "sample_index_in_group": 125}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-8_000000", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-8_000000.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -8, "sample_index_in_group": 0}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_12_000104", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_12_000104.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 12, "sample_index_in_group": 104}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-20_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-20_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -20, "sample_index_in_group": 29}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_18_000004", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_18_000004.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 18, "sample_index_in_group": 4}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 15}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_14_000006", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_14_000006.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 14, "sample_index_in_group": 6}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000043", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000043.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 43}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_0_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_0_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 0, "sample_index_in_group": 1}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_6_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_6_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 6, "sample_index_in_group": 108}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 44}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_18_000185", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_18_000185.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 18, "sample_index_in_group": 185}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 142}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-14_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-14_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -14, "sample_index_in_group": 36}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 105}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000122", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000122.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 122}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 120}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 83}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_10_000040", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_10_000040.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 10, "sample_index_in_group": 40}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000075", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000075.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 75}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_2_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_2_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 2, "sample_index_in_group": 33}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-10_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-10_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -10, "sample_index_in_group": 86}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000172", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000172.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 172}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-12_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-12_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -12, "sample_index_in_group": 133}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 189}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_10_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_10_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 10, "sample_index_in_group": 95}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-8_000184", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-8_000184.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -8, "sample_index_in_group": 184}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-20_000162", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-20_000162.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -20, "sample_index_in_group": 162}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_4_000184", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_4_000184.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 4, "sample_index_in_group": 184}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_0_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_0_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 0, "sample_index_in_group": 59}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-16_000141", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-16_000141.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -16, "sample_index_in_group": 141}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-18_000135", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-18_000135.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -18, "sample_index_in_group": 135}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 199}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000094", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000094.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 94}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 187}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_16_000195", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_16_000195.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 16, "sample_index_in_group": 195}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-14_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-14_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -14, "sample_index_in_group": 36}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 187}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_14_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_14_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 14, "sample_index_in_group": 93}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 1}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000074", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000074.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 74}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_6_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_6_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 6, "sample_index_in_group": 171}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_16_000024", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_16_000024.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 16, "sample_index_in_group": 24}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000022", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000022.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 22}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-8_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-8_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -8, "sample_index_in_group": 26}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-4_000118", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-4_000118.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -4, "sample_index_in_group": 118}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-6_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-6_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -6, "sample_index_in_group": 129}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_16_000096", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_16_000096.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 16, "sample_index_in_group": 96}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_4_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_4_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 4, "sample_index_in_group": 84}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 177}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-4_000180", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-4_000180.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -4, "sample_index_in_group": 180}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000046", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000046.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 46}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 102}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 49}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_2_000164", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_2_000164.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 2, "sample_index_in_group": 164}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_8_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_8_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 8, "sample_index_in_group": 186}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 190}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 115}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_2_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_2_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 2, "sample_index_in_group": 61}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-14_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-14_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -14, "sample_index_in_group": 53}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_14_000173", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_14_000173.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 14, "sample_index_in_group": 173}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_16_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_16_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 16, "sample_index_in_group": 77}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 128}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_18_000062", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_18_000062.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 18, "sample_index_in_group": 62}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 37}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 76}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 108}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_4_000161", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_4_000161.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 4, "sample_index_in_group": 161}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_2_000123", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_2_000123.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 2, "sample_index_in_group": 123}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 99}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-6_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-6_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -6, "sample_index_in_group": 86}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 192}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_14_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_14_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 14, "sample_index_in_group": 189}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_10_000147", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_10_000147.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 10, "sample_index_in_group": 147}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_12_000097", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_12_000097.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 12, "sample_index_in_group": 97}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-12_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-12_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -12, "sample_index_in_group": 14}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_12_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_12_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 12, "sample_index_in_group": 15}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-6_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-6_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -6, "sample_index_in_group": 29}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_6_000017", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_6_000017.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 6, "sample_index_in_group": 17}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000064", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000064.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 64}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000050", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000050.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 50}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_18_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_18_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 18, "sample_index_in_group": 78}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000137", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000137.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 137}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000007", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000007.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 7}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-4_000154", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-4_000154.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -4, "sample_index_in_group": 154}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000068", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000068.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 68}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000023", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000023.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 23}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-2_000146", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-2_000146.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -2, "sample_index_in_group": 146}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_12_000107", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_12_000107.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 12, "sample_index_in_group": 107}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 150}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000003", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000003.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 3}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_8_000188", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_8_000188.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 8, "sample_index_in_group": 188}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-18_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-18_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -18, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000025", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000025.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 25}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_4_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_4_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 4, "sample_index_in_group": 105}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 189}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_10_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_10_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 10, "sample_index_in_group": 115}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000149", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000149.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 149}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 186}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000180", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000180.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 180}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 196}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 102}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-20_000060", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-20_000060.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -20, "sample_index_in_group": 60}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 47}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-8_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-8_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -8, "sample_index_in_group": 128}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 190}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000180", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000180.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 180}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_8_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_8_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 8, "sample_index_in_group": 152}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000055", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000055.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 55}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_16_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_16_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 16, "sample_index_in_group": 84}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_6_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_6_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 6, "sample_index_in_group": 140}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000106", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000106.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 106}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000123", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000123.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 123}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_14_000160", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_14_000160.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 14, "sample_index_in_group": 160}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000041", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000041.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 41}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000127", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000127.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 127}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_14_000052", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_14_000052.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 14, "sample_index_in_group": 52}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_4_000065", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_4_000065.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 4, "sample_index_in_group": 65}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 136}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_16_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_16_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 16, "sample_index_in_group": 26}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000145", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000145.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 145}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-2_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-2_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -2, "sample_index_in_group": 93}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_18_000121", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_18_000121.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 18, "sample_index_in_group": 121}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000134", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000134.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 134}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 18}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-18_000172", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-18_000172.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -18, "sample_index_in_group": 172}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_14_000121", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_14_000121.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 14, "sample_index_in_group": 121}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 77}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-2_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-2_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -2, "sample_index_in_group": 77}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_12_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_12_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 12, "sample_index_in_group": 142}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 84}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-4_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-4_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -4, "sample_index_in_group": 131}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-4_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-4_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -4, "sample_index_in_group": 21}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-10_000185", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-10_000185.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -10, "sample_index_in_group": 185}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000000", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000000.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 0}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-20_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-20_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -20, "sample_index_in_group": 32}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 128}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000000", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000000.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 0}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 83}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 71}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000022", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000022.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 22}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_16_000178", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_16_000178.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 16, "sample_index_in_group": 178}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 190}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000137", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000137.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 137}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_14_000087", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_14_000087.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 14, "sample_index_in_group": 87}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-2_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-2_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -2, "sample_index_in_group": 49}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_18_000167", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_18_000167.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 18, "sample_index_in_group": 167}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-18_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-18_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -18, "sample_index_in_group": 92}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_0_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_0_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 0, "sample_index_in_group": 44}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000010", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000010.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 10}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-2_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-2_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -2, "sample_index_in_group": 152}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000067", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000067.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 67}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 2}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 171}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_6_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_6_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 6, "sample_index_in_group": 61}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 150}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-12_000153", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-12_000153.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -12, "sample_index_in_group": 153}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_2_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_2_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 2, "sample_index_in_group": 14}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000112", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000112.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 112}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-20_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-20_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -20, "sample_index_in_group": 47}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_12_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_12_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 12, "sample_index_in_group": 105}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_6_000117", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_6_000117.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 6, "sample_index_in_group": 117}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000087", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000087.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 87}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-16_000111", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-16_000111.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -16, "sample_index_in_group": 111}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_0_000070", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_0_000070.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 0, "sample_index_in_group": 70}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000038", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000038.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 38}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_8_000182", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_8_000182.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 8, "sample_index_in_group": 182}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-16_000056", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-16_000056.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -16, "sample_index_in_group": 56}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-12_000098", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-12_000098.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -12, "sample_index_in_group": 98}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 47}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_4_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_4_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 4, "sample_index_in_group": 197}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_6_000135", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_6_000135.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 6, "sample_index_in_group": 135}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000122", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000122.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 122}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 84}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_10_000063", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_10_000063.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 10, "sample_index_in_group": 63}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-2_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-2_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -2, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 186}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000080", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000080.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 80}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_0_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_0_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 0, "sample_index_in_group": 105}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_4_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_4_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 4, "sample_index_in_group": 136}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_8_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_8_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 8, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 36}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 131}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_4_000040", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_4_000040.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 4, "sample_index_in_group": 40}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000058", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000058.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 58}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-6_000051", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-6_000051.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -6, "sample_index_in_group": 51}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 128}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_2_000054", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_2_000054.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 2, "sample_index_in_group": 54}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000151", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000151.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 151}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 76}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_0_000161", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_0_000161.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 0, "sample_index_in_group": 161}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_8_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_8_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 8, "sample_index_in_group": 53}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_0_000181", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_0_000181.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 0, "sample_index_in_group": 181}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_6_000019", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_6_000019.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 6, "sample_index_in_group": 19}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-8_000028", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-8_000028.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -8, "sample_index_in_group": 28}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_2_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_2_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 2, "sample_index_in_group": 71}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_0_000069", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_0_000069.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 0, "sample_index_in_group": 69}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 72}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-14_000066", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-14_000066.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -14, "sample_index_in_group": 66}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_0_000119", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_0_000119.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 0, "sample_index_in_group": 119}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000069", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000069.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 69}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_4_000035", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_4_000035.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 4, "sample_index_in_group": 35}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 197}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_8_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_8_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 8, "sample_index_in_group": 99}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-16_000035", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-16_000035.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -16, "sample_index_in_group": 35}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_10_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_10_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 10, "sample_index_in_group": 59}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-6_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-6_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -6, "sample_index_in_group": 152}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_14_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_14_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 14, "sample_index_in_group": 196}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-14_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-14_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -14, "sample_index_in_group": 26}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_16_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_16_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 16, "sample_index_in_group": 138}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 142}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 86}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 2}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000096", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000096.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 96}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-16_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-16_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -16, "sample_index_in_group": 95}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-16_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-16_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -16, "sample_index_in_group": 13}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_8_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_8_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 8, "sample_index_in_group": 15}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 78}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-10_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-10_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -10, "sample_index_in_group": 83}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-18_000156", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-18_000156.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -18, "sample_index_in_group": 156}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_0_000087", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_0_000087.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 0, "sample_index_in_group": 87}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-10_000085", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-10_000085.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -10, "sample_index_in_group": 85}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-8_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-8_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -8, "sample_index_in_group": 84}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_14_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_14_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 14, "sample_index_in_group": 138}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_14_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_14_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 14, "sample_index_in_group": 15}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 150}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_18_000031", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_18_000031.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 18, "sample_index_in_group": 31}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 32}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_8_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_8_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 8, "sample_index_in_group": 133}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 171}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000139", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000139.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 139}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-6_000113", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-6_000113.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -6, "sample_index_in_group": 113}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 140}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 166}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_6_000025", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_6_000025.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 6, "sample_index_in_group": 25}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000116", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000116.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 116}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000067", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000067.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 67}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_16_000054", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_16_000054.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 16, "sample_index_in_group": 54}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000154", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000154.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 154}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-6_000154", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-6_000154.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -6, "sample_index_in_group": 154}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000073", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000073.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 73}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_12_000011", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_12_000011.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 12, "sample_index_in_group": 11}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000034", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000034.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 34}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_10_000051", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_10_000051.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 10, "sample_index_in_group": 51}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_12_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_12_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 12, "sample_index_in_group": 44}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_2_000009", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_2_000009.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 2, "sample_index_in_group": 9}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000028", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000028.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 28}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_8_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_8_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 8, "sample_index_in_group": 142}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 93}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 2}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_16_000162", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_16_000162.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 16, "sample_index_in_group": 162}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_0_000175", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_0_000175.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 0, "sample_index_in_group": 175}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-18_000183", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-18_000183.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -18, "sample_index_in_group": 183}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000161", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000161.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 161}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 186}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000070", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000070.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 70}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 129}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-20_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-20_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -20, "sample_index_in_group": 8}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 84}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-18_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-18_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -18, "sample_index_in_group": 71}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_10_000178", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_10_000178.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 10, "sample_index_in_group": 178}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_12_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_12_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 12, "sample_index_in_group": 8}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_12_000109", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_12_000109.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 12, "sample_index_in_group": 109}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_18_000135", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_18_000135.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 18, "sample_index_in_group": 135}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 165}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-10_000168", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-10_000168.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -10, "sample_index_in_group": 168}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-2_000058", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-2_000058.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -2, "sample_index_in_group": 58}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 177}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000040", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000040.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 40}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 197}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-18_000060", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-18_000060.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -18, "sample_index_in_group": 60}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_2_000098", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_2_000098.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 2, "sample_index_in_group": 98}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-8_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-8_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -8, "sample_index_in_group": 15}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-4_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-4_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -4, "sample_index_in_group": 129}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-6_000082", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-6_000082.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -6, "sample_index_in_group": 82}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_4_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_4_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 4, "sample_index_in_group": 53}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000007", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000007.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 7}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 76}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000184", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000184.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 184}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 199}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 186}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_8_000147", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_8_000147.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 8, "sample_index_in_group": 147}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000174", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000174.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 174}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_12_000100", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_12_000100.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 12, "sample_index_in_group": 100}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_16_000181", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_16_000181.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 16, "sample_index_in_group": 181}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_16_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_16_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 16, "sample_index_in_group": 108}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_8_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_8_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 8, "sample_index_in_group": 59}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_12_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_12_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 12, "sample_index_in_group": 166}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 44}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-2_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-2_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -2, "sample_index_in_group": 79}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000106", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000106.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 106}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_12_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_12_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 12, "sample_index_in_group": 115}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000185", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000185.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 185}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 81}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-14_000069", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-14_000069.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -14, "sample_index_in_group": 69}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-16_000019", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-16_000019.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -16, "sample_index_in_group": 19}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_16_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_16_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 16, "sample_index_in_group": 165}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000163", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000163.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 163}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-4_000035", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-4_000035.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -4, "sample_index_in_group": 35}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-16_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-16_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -16, "sample_index_in_group": 115}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-8_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-8_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -8, "sample_index_in_group": 92}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_6_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_6_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 6, "sample_index_in_group": 99}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 37}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 138}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_10_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_10_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 10, "sample_index_in_group": 93}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000005", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000005.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 5}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_6_000003", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_6_000003.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 6, "sample_index_in_group": 3}}
+{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000179", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000179.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 179}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "14__10_07_13_piraCies_Espera_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/14__10_07_13_piraCies_Espera_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "14__10_07_13_piraCies_Espera.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "14__10_07_13_piraCies_Espera_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/14__10_07_13_piraCies_Espera_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "14__10_07_13_piraCies_Espera.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "14__10_07_13_piraCies_Espera_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/14__10_07_13_piraCies_Espera_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "14__10_07_13_piraCies_Espera.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "7__10_07_13_marDeCangas_Espera_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/7__10_07_13_marDeCangas_Espera_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "7__10_07_13_marDeCangas_Espera.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "7__10_07_13_marDeCangas_Espera_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/7__10_07_13_marDeCangas_Espera_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "7__10_07_13_marDeCangas_Espera.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "7__10_07_13_marDeCangas_Espera_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/7__10_07_13_marDeCangas_Espera_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "7__10_07_13_marDeCangas_Espera.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "21__18_07_13_lanchaMotora_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/21__18_07_13_lanchaMotora_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "21__18_07_13_lanchaMotora.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "20__18_07_13_AutopridePrepManiobra_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/20__18_07_13_AutopridePrepManiobra_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "20__18_07_13_AutopridePrepManiobra.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "40__19_07_13_MarDeCangas_Llega_Interf_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/40__19_07_13_MarDeCangas_Llega_Interf_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "40__19_07_13_MarDeCangas_Llega_Interf.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "43__19_07_13_PirataDeCies_Llega_Interf_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/43__19_07_13_PirataDeCies_Llega_Interf_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "43__19_07_13_PirataDeCies_Llega_Interf.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "43__19_07_13_PirataDeCies_Llega_Interf_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/43__19_07_13_PirataDeCies_Llega_Interf_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "43__19_07_13_PirataDeCies_Llega_Interf.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "28__19_07_13_NuevoRiaAldan_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/28__19_07_13_NuevoRiaAldan_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Trawler.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Trawler"]}, "meta_data": {"ori_file_path": "28__19_07_13_NuevoRiaAldan.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "31__19_07_13_RemolcadorArrancaPara_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/31__19_07_13_RemolcadorArrancaPara_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Tugboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Tugboat"]}, "meta_data": {"ori_file_path": "31__19_07_13_RemolcadorArrancaPara.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "31__19_07_13_RemolcadorArrancaPara_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/31__19_07_13_RemolcadorArrancaPara_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Tugboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Tugboat"]}, "meta_data": {"ori_file_path": "31__19_07_13_RemolcadorArrancaPara.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "31__19_07_13_RemolcadorArrancaPara_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/31__19_07_13_RemolcadorArrancaPara_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Tugboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Tugboat"]}, "meta_data": {"ori_file_path": "31__19_07_13_RemolcadorArrancaPara.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "30__19_07_13_practico2_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/30__19_07_13_practico2_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Pilot boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Pilot boat"]}, "meta_data": {"ori_file_path": "30__19_07_13_practico2.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "24__19_07_13_adventureFrenando_duda_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/24__19_07_13_adventureFrenando_duda_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Ocean liner.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Ocean liner"]}, "meta_data": {"ori_file_path": "24__19_07_13_adventureFrenando_duda.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "22__19_07_13_adventure_maniobra_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/22__19_07_13_adventure_maniobra_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Ocean liner.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Ocean liner"]}, "meta_data": {"ori_file_path": "22__19_07_13_adventure_maniobra.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "22__19_07_13_adventure_maniobra_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/22__19_07_13_adventure_maniobra_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Ocean liner.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Ocean liner"]}, "meta_data": {"ori_file_path": "22__19_07_13_adventure_maniobra.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 4}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "54__23_07_13_H3_PirataSalvora_SALE1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/54__23_07_13_H3_PirataSalvora_SALE1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "54__23_07_13_H3_PirataSalvora_SALE1.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "54__23_07_13_H3_PirataSalvora_SALE1_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/54__23_07_13_H3_PirataSalvora_SALE1_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "54__23_07_13_H3_PirataSalvora_SALE1.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "54__23_07_13_H3_PirataSalvora_SALE1_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/54__23_07_13_H3_PirataSalvora_SALE1_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "54__23_07_13_H3_PirataSalvora_SALE1.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "56__23_07_13_H3_velero1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/56__23_07_13_H3_velero1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Sailboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Sailboat"]}, "meta_data": {"ori_file_path": "56__23_07_13_H3_velero1.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "56__23_07_13_H3_velero1_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/56__23_07_13_H3_velero1_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Sailboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Sailboat"]}, "meta_data": {"ori_file_path": "56__23_07_13_H3_velero1.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 4}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 4}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 5}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 6}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg7", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg7.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 7}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg8", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg8.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 8}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 4}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 5}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 6}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg7", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg7.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 7}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "75__23_07_13_H3_pesquero1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/75__23_07_13_H3_pesquero1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Fish boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Fish boat"]}, "meta_data": {"ori_file_path": "75__23_07_13_H3_pesquero1.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "75__23_07_13_H3_pesquero1_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/75__23_07_13_H3_pesquero1_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Fish boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Fish boat"]}, "meta_data": {"ori_file_path": "75__23_07_13_H3_pesquero1.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 4}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 5}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 6}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg7", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg7.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 7}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg8", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg8.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 8}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg9", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg9.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 9}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg10", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg10.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 10}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 4}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 2}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 3}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 4}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 5}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 6}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "93__A__Draga_1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/93__A__Draga_1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "93__A__Draga_1.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "95__A__Draga_3_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/95__A__Draga_3_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "95__A__Draga_3.wav", "seg_index": 0}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "95__A__Draga_3_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/95__A__Draga_3_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "95__A__Draga_3.wav", "seg_index": 1}}
+{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "95__A__Draga_3_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/95__A__Draga_3_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "95__A__Draga_3.wav", "seg_index": 2}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00843", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00843.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161022-44_030503_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161022-44_030503_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00204", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00204.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00291", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00291.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170429a-68_102113_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170429a-68_102113_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180418-193_004704_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180418-193_004704_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161113a-53_212239_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161113a-53_212239_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00960", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00960.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180411-189_081047_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180411-189_081047_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00534", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00534.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00402", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00402.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00195.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160507a-4_4_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160507a-4_4_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00159", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00159.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180226-180_033811_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180226-180_033811_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00783", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00783.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00369", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00369.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170530a-87_165315_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170530a-87_165315_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00948", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00948.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170520a-84_174851_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170520a-84_174851_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160823-31_041452_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160823-31_041452_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161022-44_030503_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161022-44_030503_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00195.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221-61_114923_00213", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221-61_114923_00213.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161009-42_42_00021", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161009-42_42_00021.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00321", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00321.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160521a-10_041411_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160521a-10_041411_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170625-118_180527_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170625-118_180527_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170517a-82_162940_00018", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170517a-82_162940_00018.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171214a-101_075400_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171214a-101_075400_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215b-108_114505_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215b-108_114505_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00093", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00093.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171106-4_4_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171106-4_4_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00435", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00435.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170122b-74_050415_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170122b-74_050415_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00201", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00201.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00477", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00477.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180610-215_135143_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180610-215_135143_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00453", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00453.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00489", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00489.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00459", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00459.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170629-120_215813_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170629-120_215813_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00729", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00729.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170510-105_041848_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170510-105_041848_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905b-182_061547_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905b-182_061547_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00426", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00426.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00456", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00456.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905b-182_061547_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905b-182_061547_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00678", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00678.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180219-145_034422_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180219-145_034422_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00489", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00489.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00450", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00450.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408a-66_224944_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408a-66_224944_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210c-90_124043_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210c-90_124043_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00351", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00351.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171210-135_021603_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171210-135_021603_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161002-40_130331_00111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161002-40_130331_00111.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00411", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00411.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160629a-23_23_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160629a-23_23_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00504", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00504.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160629a-23_23_00111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160629a-23_23_00111.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00486", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00486.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00408", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00408.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170510-75_113409_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170510-75_113409_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00273", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00273.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00651", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00651.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00204", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00204.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00135.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170608-113_234244_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170608-113_234244_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171115-9_9_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171115-9_9_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128c-28_190038_00270", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128c-28_190038_00270.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00465", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00465.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00477", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00477.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00234", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00234.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160521a-10_041411_00234", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160521a-10_041411_00234.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00381", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00381.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00438", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00438.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00630", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00630.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170111-51_193600_00201", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170111-51_193600_00201.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00198.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00198.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210c-90_124043_00321", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210c-90_124043_00321.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205-69_69_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205-69_69_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00501", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00501.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171125c-46_134833_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171125c-46_134833_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00144.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207-79_010450_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207-79_010450_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180219-145_034422_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180219-145_034422_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00867", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00867.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00441", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00441.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207-79_010450_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207-79_010450_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171210-135_021603_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171210-135_021603_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00273", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00273.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00453", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00453.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00324", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00324.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00405", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00405.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00660", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00660.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170517b-83_225337_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170517b-83_225337_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170417-67_025741_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170417-67_025741_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00147", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00147.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170122b-74_050415_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170122b-74_050415_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124d-43_161634_00297", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124d-43_161634_00297.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00939", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00939.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160831-38_034757_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160831-38_034757_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00111.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180914-234_024440_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180914-234_024440_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171203a-40_40_00147", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171203a-40_40_00147.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00003.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00612", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00612.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160831-38_034757_00198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160831-38_034757_00198.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00231", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00231.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161027-46_145435_00225", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161027-46_145435_00225.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170401-64_000636_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170401-64_000636_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160927-39_39_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160927-39_39_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170530a-87_165315_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170530a-87_165315_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00606", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00606.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00639", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00639.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170209-80_040238_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170209-80_040238_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171129-55_000645_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171129-55_000645_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00267", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00267.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00306", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00306.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170510-75_113409_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170510-75_113409_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00018", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00018.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00267", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00267.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170120-70_070941_00147", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170120-70_070941_00147.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00765", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00765.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180503-159_153434_00318", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180503-159_153434_00318.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00588", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00588.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171115-9_9_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171115-9_9_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00306", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00306.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00447", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00447.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00543", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00543.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00165.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00192", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00192.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00363", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00363.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160601c-14_14_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160601c-14_14_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00345", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00345.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00411", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00411.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180219-145_034422_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180219-145_034422_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00276", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00276.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00159", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00159.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171111-7_025236_00375", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171111-7_025236_00375.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180125-140_071504_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180125-140_071504_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00201", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00201.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00537", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00537.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00423", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00423.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_01023", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_01023.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_01128", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_01128.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00207", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00207.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00405", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00405.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00444", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00444.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00318", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00318.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170608-113_234244_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170608-113_234244_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00252", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00252.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00183.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00444", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00444.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180203-174_232020_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180203-174_232020_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00483", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00483.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161022-44_030503_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161022-44_030503_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180610-215_135143_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180610-215_135143_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160507a-4_4_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160507a-4_4_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00312", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00312.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00021", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00021.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160831-38_034757_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160831-38_034757_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00384", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00384.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00645", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00645.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180503-159_153434_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180503-159_153434_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160823-31_041452_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160823-31_041452_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160629a-23_23_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160629a-23_23_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111f-10_10_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111f-10_10_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00156", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00156.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205-69_69_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205-69_69_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00534", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00534.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124d-43_161634_00165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124d-43_161634_00165.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180226-180_033811_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180226-180_033811_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171106-4_4_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171106-4_4_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00321", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00321.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171214a-101_075400_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171214a-101_075400_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00300", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00300.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171214a-101_075400_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171214a-101_075400_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00573", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00573.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00492", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00492.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00594", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00594.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171118-27_27_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171118-27_27_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205e-71_111659_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205e-71_111659_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180703-177_221012_00195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180703-177_221012_00195.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124d-43_161634_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124d-43_161634_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00366", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00366.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00291", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00291.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00222", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00222.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171120a-18_054508_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171120a-18_054508_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170417-67_025741_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170417-67_025741_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00309", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00309.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161002-40_130331_00228", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161002-40_130331_00228.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170209-80_040238_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170209-80_040238_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00309", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00309.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00363", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00363.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00630", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00630.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00267", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00267.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00135.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00165.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171206e-78_78_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171206e-78_78_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00189", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00189.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180418-193_004704_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180418-193_004704_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180418-193_004704_00396", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180418-193_004704_00396.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00546", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00546.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00393", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00393.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00435", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00435.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00423", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00423.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00222", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00222.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00156", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00156.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180125-140_071504_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180125-140_071504_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00102.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00102.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00288", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00288.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00459", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00459.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170530a-87_165315_00192", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170530a-87_165315_00192.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171129-55_000645_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171129-55_000645_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616a-95_231343_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616a-95_231343_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00306", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00306.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207-79_010450_00135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207-79_010450_00135.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170122b-74_050415_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170122b-74_050415_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00375", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00375.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171204-66_024305_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171204-66_024305_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205e-71_111659_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205e-71_111659_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00183.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161024-45_061514_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161024-45_061514_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161102-48_124450_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161102-48_124450_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00417", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00417.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00276", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00276.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171118-27_27_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171118-27_27_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00336", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00336.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128c-28_190038_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128c-28_190038_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171125c-46_134833_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171125c-46_134833_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111f-10_10_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111f-10_10_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00354", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00354.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00360", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00360.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00357", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00357.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00450", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00450.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00756", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00756.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170331-62_044959_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170331-62_044959_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00204", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00204.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170906-114_104632_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170906-114_104632_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170510-75_113409_00222", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170510-75_113409_00222.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00213", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00213.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00774", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00774.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170111-51_193600_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170111-51_193600_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180203-174_232020_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180203-174_232020_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00684", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00684.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00543", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00543.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180120-139_191134_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180120-139_191134_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00432", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00432.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170111-51_193600_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170111-51_193600_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00171", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00171.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170403a-93_102955_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170403a-93_102955_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171115-9_9_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171115-9_9_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170625-118_180527_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170625-118_180527_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180703-177_221012_00183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180703-177_221012_00183.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00366", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00366.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180326-149_180641_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180326-149_180641_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180206a-176_131726_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180206a-176_131726_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161009-42_42_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161009-42_42_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00663", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00663.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171118-27_27_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171118-27_27_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171120a-18_054508_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171120a-18_054508_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00288", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00288.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00039", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00039.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170512-107_081819_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170512-107_081819_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00768", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00768.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00600", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00600.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00300", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00300.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180703-177_221012_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180703-177_221012_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171210-135_021603_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171210-135_021603_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205e-71_111659_00213", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205e-71_111659_00213.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00750", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00750.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171111-7_025236_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171111-7_025236_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00408", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00408.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00378", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00378.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00324", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00324.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170417-67_025741_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170417-67_025741_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160529-13_083740_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160529-13_083740_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180827-180_182309_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180827-180_182309_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180119-167_055815_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180119-167_055815_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00120", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00120.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00189", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00189.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00192", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00192.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00534", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00534.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00342", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00342.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00225", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00225.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00456", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00456.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170906-114_104632_00504", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170906-114_104632_00504.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00252", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00252.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180413-191_212814_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180413-191_212814_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160601c-14_14_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160601c-14_14_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00342", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00342.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221-61_114923_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221-61_114923_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171204-66_024305_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171204-66_024305_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160529-13_083740_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160529-13_083740_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00144.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161002-40_130331_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161002-40_130331_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160529-13_083740_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160529-13_083740_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210c-90_124043_00333", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210c-90_124043_00333.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00345", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00345.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161027-46_145435_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161027-46_145435_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00960", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00960.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160521a-10_041411_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160521a-10_041411_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00462", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00462.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00675", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00675.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00156", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00156.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170430-69_011712_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170430-69_011712_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
+{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "468144", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Phee_468144.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "291796", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Seep_291796.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "527671", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Infant_cry_527671.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "51999", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_5_Phee_51999.flac", "length": 216000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "292987", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Tsik_292987.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "822093", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Phee_822093.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "495470", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Phee_495470.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "530733", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_0_Phee_530733.flac", "length": 148801, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "333626", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Infant_cry_333626.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "405547", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Twitter_405547.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "490682", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_0_Trill_490682.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "477589", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Phee_477589.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "694179", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_0_Trill_694179.flac", "length": 115199, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "778022", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_6_Infant_cry_778022.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "440340", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_4_Infant_cry_440340.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "480180", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Tsik_480180.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "859866", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Phee_859866.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "548311", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Phee_548311.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "200536", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_0_Trill_200536.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "637260", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Infant_cry_637260.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "648203", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_648203.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "537062", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Trill_537062.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "465730", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Trill_465730.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "602236", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_1_Tsik_602236.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "81258", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Seep_81258.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "65428", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_0_Tsik_65428.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "792306", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Phee_792306.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "842177", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Phee_842177.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "684457", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Phee_684457.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "244290", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Trill_244290.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "315587", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Trill_315587.flac", "length": 105600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "622493", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Tsik_622493.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "824367", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_7_Tsik_824367.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "236301", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Twitter_236301.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "340467", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Phee_340467.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "713214", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_2_Twitter_713214.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "214245", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Seep_214245.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "563222", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Seep_563222.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "307253", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Infant_cry_307253.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "241934", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Tsik_241934.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "845797", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_9_Twitter_845797.flac", "length": 196800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "494941", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_2_Trill_494941.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "324416", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_324416.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "161384", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_1_Phee_161384.flac", "length": 206400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "296420", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Infant_cry_296420.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "607130", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_0_Twitter_607130.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "648280", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Phee_648280.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "677488", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Phee_677488.flac", "length": 216000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "89947", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Seep_89947.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "344098", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Twitter_344098.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "257025", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Trill_257025.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "307748", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Twitter_307748.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "213411", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2019_12_0_Seep_213411.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "520676", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Phee_520676.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "775329", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_6_Twitter_775329.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "537095", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Phee_537095.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "568419", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_9_0_Tsik_568419.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "495419", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Seep_495419.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "599516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_0_Trill_599516.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "627130", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_1_Phee_627130.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "198878", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Infant_cry_198878.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "67886", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_5_Phee_67886.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "317565", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_317565.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "463312", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Trill_463312.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "560241", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Tsik_560241.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "226242", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Twitter_226242.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "32507", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Seep_32507.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "142549", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_8_Seep_142549.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "49473", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_4_Tsik_49473.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "558544", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Infant_cry_558544.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "780636", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_6_Trill_780636.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "572806", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Twitter_572806.flac", "length": 240000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "246528", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Seep_246528.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "513669", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Tsik_513669.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "202898", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Twitter_202898.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "561744", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Tsik_561744.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "517463", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Seep_517463.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "718737", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Tsik_718737.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "553385", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_553385.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "529002", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Seep_529002.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "311566", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Trill_311566.flac", "length": 134400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "315445", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Seep_315445.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "421881", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_2_Tsik_421881.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "644865", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Twitter_644865.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "595029", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Phee_595029.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "556764", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Seep_556764.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "30062", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Trill_30062.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "238036", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Twitter_238036.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "27721", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_2_Phee_27721.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "679249", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_2_Twitter_679249.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "236768", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Phee_236768.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "88450", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Twitter_88450.flac", "length": 119999, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "670886", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_2_Trill_670886.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "497761", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_2_Trill_497761.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "127394", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_6_Trill_127394.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "245126", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Tsik_245126.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "683306", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Infant_cry_683306.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "527901", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Seep_527901.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "845235", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_9_Tsik_845235.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "136336", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Infant_cry_136336.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "508718", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Tsik_508718.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "624021", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Trill_624021.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "558376", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Tsik_558376.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "734754", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_4_Twitter_734754.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "369493", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Tsik_369493.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "867739", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Twitter_867739.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "345384", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Infant_cry_345384.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "549757", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Phee_549757.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "671625", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_2_Trill_671625.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "504829", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Infant_cry_504829.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "401075", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_2_Seep_401075.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "583598", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Phee_583598.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "31100", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Tsik_31100.flac", "length": 177599, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "642729", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Phee_642729.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "157428", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_0_Seep_157428.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "383567", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_0_Twitter_383567.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "861427", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Infant_cry_861427.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "505477", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Seep_505477.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "553755", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Seep_553755.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "537527", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Trill_537527.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "17610", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_1_Phee_17610.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "318650", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_318650.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "123168", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_6_Twitter_123168.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "69736", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_1_Tsik_69736.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "655516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_1_Phee_655516.flac", "length": 148799, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "839626", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Seep_839626.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "5268", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_0_Twitter_5268.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "502249", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Twitter_502249.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "429151", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Phee_429151.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "346323", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Seep_346323.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "60235", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_0_Tsik_60235.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "416347", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_2_Trill_416347.flac", "length": 100801, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "591105", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Seep_591105.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "266018", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Infant_cry_266018.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "665412", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Infant_cry_665412.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "819374", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Phee_819374.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "141114", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Twitter_141114.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "818684", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Tsik_818684.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "320493", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_320493.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "568941", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Infant_cry_568941.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "279620", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Tsik_279620.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "484529", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_7_Seep_484529.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "7747", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_0_Phee_7747.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "597641", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Trill_597641.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "287515", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Infant_cry_287515.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "667425", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Trill_667425.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "529199", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Tsik_529199.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "153342", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_0_Trill_153342.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "589601", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Seep_589601.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "812228", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Seep_812228.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "461401", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Infant_cry_461401.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "720228", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_9_0_Tsik_720228.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "153077", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_8_Seep_153077.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "796030", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Tsik_796030.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "863950", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_2_Tsik_863950.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "760154", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Twitter_760154.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "50439", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_5_Phee_50439.flac", "length": 177599, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "30684", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Twitter_30684.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "606103", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_1_Tsik_606103.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "298516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Infant_cry_298516.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "569210", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Seep_569210.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "817251", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Tsik_817251.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "870190", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Trill_870190.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "353285", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_12_Tsik_353285.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "484798", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_7_Infant_cry_484798.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "448140", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Seep_448140.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "322838", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_322838.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "176152", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_2_Seep_176152.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "614539", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_1_Infant_cry_614539.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "247127", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_0_Tsik_247127.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "599694", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Infant_cry_599694.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "625997", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Infant_cry_625997.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "833741", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Twitter_833741.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "809147", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Twitter_809147.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "227276", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Phee_227276.flac", "length": 167999, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "366636", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Trill_366636.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "553634", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Tsik_553634.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "602999", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_1_Phee_602999.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "153060", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_8_Seep_153060.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "678999", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_2_Twitter_678999.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "94205", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_3_Trill_94205.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "74516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_1_Twitter_74516.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "321412", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Tsik_321412.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "822756", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_7_Twitter_822756.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "253953", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Twitter_253953.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "219637", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_0_Seep_219637.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "804950", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Phee_804950.flac", "length": 148801, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "211621", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_0_Phee_211621.flac", "length": 120001, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "753310", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Seep_753310.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "799623", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Seep_799623.flac", "length": 201600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "539420", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_3_0_Phee_539420.flac", "length": 240000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "759941", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Tsik_759941.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "118185", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_5_Trill_118185.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "14743", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_1_Seep_14743.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "548949", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Tsik_548949.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "637217", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_637217.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "799534", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Infant_cry_799534.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "834507", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Twitter_834507.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "854779", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_10_Twitter_854779.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "558322", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Infant_cry_558322.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "638770", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_1_Phee_638770.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "608229", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Seep_608229.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "86656", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Twitter_86656.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "731523", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_1_Twitter_731523.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "290146", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Infant_cry_290146.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "31619", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Phee_31619.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "687025", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_2_Trill_687025.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "215932", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Seep_215932.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "206863", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_0_Tsik_206863.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "425592", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_0_Tsik_425592.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "564773", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Infant_cry_564773.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "766331", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_5_Infant_cry_766331.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "786639", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_2_Tsik_786639.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "312988", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Infant_cry_312988.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "621108", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_2_Tsik_621108.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "868322", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Seep_868322.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "137048", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Infant_cry_137048.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "332721", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Twitter_332721.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "683581", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Seep_683581.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "505351", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Tsik_505351.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "411681", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Twitter_411681.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "476207", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Trill_476207.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "183832", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_3_Trill_183832.flac", "length": 201600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "587418", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Trill_587418.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "366071", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Seep_366071.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "277794", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_0_Twitter_277794.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "279461", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Tsik_279461.flac", "length": 144000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "335621", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_1_Infant_cry_335621.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "282348", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Phee_282348.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "237777", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Seep_237777.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "551789", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Tsik_551789.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "817888", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Twitter_817888.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "716276", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Trill_716276.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "584058", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Tsik_584058.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "509317", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Trill_509317.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "128751", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_6_Tsik_128751.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "287525", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Infant_cry_287525.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "552157", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Tsik_552157.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "136453", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Phee_136453.flac", "length": 196800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "195628", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_0_Seep_195628.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "795548", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Phee_795548.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "15028", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_1_Twitter_15028.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "790812", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Infant_cry_790812.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "201787", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Infant_cry_201787.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "369017", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Infant_cry_369017.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "650896", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_1_Seep_650896.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "32801", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Twitter_32801.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "243265", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Seep_243265.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "170769", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_1_Infant_cry_170769.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "296993", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Seep_296993.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "761764", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_2_Twitter_761764.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "858188", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_10_Trill_858188.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "68450", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_0_Phee_68450.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "689436", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Infant_cry_689436.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "487399", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_7_Phee_487399.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "668696", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Trill_668696.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "140935", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Infant_cry_140935.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "233014", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_0_Trill_233014.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "330142", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Twitter_330142.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "340127", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Twitter_340127.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "581220", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_581220.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "300282", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Seep_300282.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "624123", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Tsik_624123.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "576659", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Infant_cry_576659.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "329730", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Seep_329730.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "419822", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_2_Phee_419822.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "31590", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Phee_31590.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "717590", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Trill_717590.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "167990", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_1_Phee_167990.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "739708", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_3_Phee_739708.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "660121", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Tsik_660121.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "251135", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Trill_251135.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "868149", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Seep_868149.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "806704", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Trill_806704.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "475392", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Twitter_475392.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "465712", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Phee_465712.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "594447", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Infant_cry_594447.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "355954", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_1_Seep_355954.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "524184", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_0_Phee_524184.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "437308", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_1_Twitter_437308.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "332615", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Twitter_332615.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "628314", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_1_Infant_cry_628314.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "383286", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Twitter_383286.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "299965", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Trill_299965.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "531940", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Trill_531940.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "473834", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Infant_cry_473834.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "409628", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Trill_409628.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "623889", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Trill_623889.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "869766", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Seep_869766.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "844416", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_9_Twitter_844416.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "278688", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Trill_278688.flac", "length": 144000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "770396", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_5_Tsik_770396.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "643271", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Twitter_643271.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "443323", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_1_Trill_443323.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "137811", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Twitter_137811.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "267073", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Phee_267073.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "279463", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Tsik_279463.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "220995", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Infant_cry_220995.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "212233", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Infant_cry_212233.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
+{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "665138", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Seep_665138.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_2_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_2_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_2_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000~2000_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000~2000_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000~2000_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1797_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1797_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1797_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1797_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1797_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1797_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_60_2_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_60_2_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_60_2_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_2_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_2_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_2_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_1_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_1_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_1_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_20_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_20_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_20_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_800~1500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_800~1500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_800~1500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500~2500_60_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500~2500_60_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500~2500_60_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500~2500_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500~2500_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500~2500_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_2_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_2_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_2_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_1_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_1_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_1_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_800-1500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_800-1500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_800-1500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1797_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1797_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1797_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_3000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_3000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_3000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_1_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_1_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_1_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000~2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000~2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000~2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_20_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_20_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_20_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_1_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_1_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_1_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_60_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_60_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_60_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1797_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1797_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1797_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2500_20_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2500_20_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2500_20_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_20_2_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_20_2_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_20_2_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_1_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_1_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_1_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_20_1_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_20_1_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_20_1_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_60_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_60_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_60_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_60_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_60_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_60_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_60_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_60_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_60_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_20_3_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_20_3_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_20_3_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500~2500_20_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500~2500_20_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500~2500_20_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_0_1_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_0_1_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_0_1_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_20_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_20_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_20_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_2_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_2_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_2_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_3000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_3000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_3000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_20_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_20_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_20_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_20_2_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_20_2_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_20_2_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1797_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1797_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1797_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_0_2_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_0_2_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_0_2_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_2_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_2_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_2_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_3000_20_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_3000_20_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_3000_20_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_40_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_40_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_40_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_60_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_60_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_60_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_20_1_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_20_1_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_20_1_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1800_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1800_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1800_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_2_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_2_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_2_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_3000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_3000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_3000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_1_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_1_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_1_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_20_1_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_20_1_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_20_1_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_40_2_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_40_2_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_40_2_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500~2500_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500~2500_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500~2500_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_800-1500_60_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_800-1500_60_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_800-1500_60_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_20_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_20_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_20_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_60_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_60_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_60_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_20_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_20_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_20_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2500_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2500_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2500_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_60_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_60_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_60_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1797_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1797_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1797_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2500_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2500_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2500_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_20_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_20_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_20_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_60_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_60_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_60_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_2_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_2_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_2_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_60_2_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_60_2_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_60_2_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_40_2_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_40_2_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_40_2_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_40_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_40_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_40_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_800~1500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_800~1500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_800~1500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_800~1500_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_800~1500_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_800~1500_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1800_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1800_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1800_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_3000_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_3000_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_3000_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_20_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_20_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_20_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2000_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2000_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2000_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_60_2_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_60_2_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_60_2_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_0_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1797_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1797_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1797_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1797_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1797_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1797_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1797_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1797_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1797_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000~2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000~2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000~2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500~2500_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500~2500_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500~2500_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_20_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_20_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_20_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_1_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_1_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_1_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_20_2_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_20_2_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_20_2_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_2_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_2_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_2_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_20_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_20_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_20_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_9_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_12_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_2_channel3.wav", "length": 256087, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_4_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_13_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N09_M07_F10_K003_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N09_M07_F10_K003_19_channel2.wav", "length": 256867, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N09_M07_F10_K003_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_18_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_15_channel2.wav", "length": 256093, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_4_channel3.wav", "length": 256079, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_5_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_8_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N09_M07_F10_K004_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N09_M07_F10_K004_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N09_M07_F10_K004_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_5_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_16_channel2.wav", "length": 256090, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_3_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_10_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F10_KI18_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F10_KI18_13_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F10_KI18_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_4_channel3.wav", "length": 256388, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_15_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_6_channel2.wav", "length": 256593, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_16_channel2.wav", "length": 256176, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_14_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N09_M07_F10_KA22_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N09_M07_F10_KA22_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N09_M07_F10_KA22_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N09_M07_F10_KI21_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N09_M07_F10_KI21_19_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N09_M07_F10_KI21_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_4_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_5_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_13_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_19_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_16_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_18_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_4_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_1_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_16_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_19_channel2.wav", "length": 256951, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_7_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_7_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_13_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_19_channel3.wav", "length": 256081, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_3_channel2.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_8_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N09_M07_F10_KI01_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N09_M07_F10_KI01_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N09_M07_F10_KI01_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_18_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_20_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_6_channel2.wav", "length": 256075, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_19_channel3.wav", "length": 261419, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_2_channel2.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_4_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_15_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_12_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_3_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_11_channel2.wav", "length": 256018, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_7_channel3.wav", "length": 256125, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_1_channel2.wav", "length": 257207, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_14_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_7_channel2.wav", "length": 256094, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_3_channel2.wav", "length": 256005, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_17_channel2.wav", "length": 256104, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_17_channel2.wav", "length": 256068, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_15_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_7_channel3.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_11_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N09_M07_F10_K004_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N09_M07_F10_K004_16_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N09_M07_F10_K004_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_19_channel2.wav", "length": 257691, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_3_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_8_channel2.wav", "length": 255998, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_2_channel3.wav", "length": 260765, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_11_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_8_channel3.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_16_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_8_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_5_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_17_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_10_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F10_KI18_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F10_KI18_19_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F10_KI18_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_3_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N15_M07_F04_KA30_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N15_M07_F04_KA30_10_channel2.wav", "length": 256070, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N15_M07_F04_KA30_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_17_channel2.wav", "length": 256005, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N09_M07_F10_K003_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N09_M07_F10_K003_20_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N09_M07_F10_K003_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_6_channel2.wav", "length": 256293, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_13_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_8_channel3.wav", "length": 280916, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_2_channel3.wav", "length": 256086, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_18_channel2.wav", "length": 256107, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M01_F10_KA05_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M01_F10_KA05_12_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M01_F10_KA05_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_12_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_7_channel3.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N09_M07_F10_KA22_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N09_M07_F10_KA22_6_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N09_M07_F10_KA22_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_5_channel2.wav", "length": 261433, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_4_channel2.wav", "length": 256095, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_7_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_14_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_4_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_8_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_11_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_2_channel3.wav", "length": 256091, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N09_M07_F10_KA05_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N09_M07_F10_KA05_10_channel2.wav", "length": 256086, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N09_M07_F10_KA05_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_5_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_3_channel2.wav", "length": 256093, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_1_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M01_F10_KA05_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M01_F10_KA05_5_channel3.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M01_F10_KA05_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_20_channel3.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N09_M07_F10_KA22_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N09_M07_F10_KA22_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N09_M07_F10_KA22_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_20_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_15_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_11_channel2.wav", "length": 249940, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_2_channel3.wav", "length": 256078, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_6_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_2_channel2.wav", "length": 256082, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_18_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_19_channel3.wav", "length": 256106, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_6_channel3.wav", "length": 256724, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_7_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N15_M07_F10_KI16_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N15_M07_F10_KI16_9_channel2.wav", "length": 274134, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N15_M07_F10_KI16_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M01_F10_KA05_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M01_F10_KA05_16_channel2.wav", "length": 256075, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M01_F10_KA05_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_15_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M07_F10_KA01_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M07_F10_KA01_20_channel3.wav", "length": 256113, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M07_F10_KA01_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_9_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N09_M07_F10_KI01_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N09_M07_F10_KI01_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N09_M07_F10_KI01_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_5_channel2.wav", "length": 261042, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N15_M07_F04_KI08_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N15_M07_F04_KI08_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N15_M07_F04_KI08_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_3_channel2.wav", "length": 256135, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_2_channel3.wav", "length": 256080, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N15_M07_F04_KI08_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N15_M07_F04_KI08_18_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N15_M07_F04_KI08_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_13_channel2.wav", "length": 255997, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_7_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_6_channel3.wav", "length": 256091, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_3_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_17_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N15_M07_F04_KA30_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N15_M07_F04_KA30_2_channel3.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N15_M07_F04_KA30_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_16_channel2.wav", "length": 262735, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_4_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_16_channel3.wav", "length": 256087, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_16_channel3.wav", "length": 259404, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_14_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_6_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_4_channel3.wav", "length": 256080, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_20_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_9_channel2.wav", "length": 256078, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N15_M07_F04_KI01_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N15_M07_F04_KI01_9_channel2.wav", "length": 256246, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N15_M07_F04_KI01_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_12_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_16_channel3.wav", "length": 256673, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_4_channel2.wav", "length": 277358, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_19_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_2_channel2.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_14_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N15_M07_F10_KI16_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N15_M07_F10_KI16_3_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N15_M07_F10_KI16_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_20_channel2.wav", "length": 257678, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_20_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_14_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_6_channel3.wav", "length": 256575, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_17_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_17_channel3.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_17_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N09_M07_F10_KA01_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N09_M07_F10_KA01_16_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N09_M07_F10_KA01_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_19_channel2.wav", "length": 256006, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_16_channel3.wav", "length": 250604, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_1_channel2.wav", "length": 258669, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_17_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_10_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_7_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_1_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_5_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_13_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M07_F10_KA01_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M07_F10_KA01_1_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M07_F10_KA01_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N09_M07_F10_K003_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N09_M07_F10_K003_8_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N09_M07_F10_K003_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N15_M07_F04_KI01_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N15_M07_F04_KI01_6_channel2.wav", "length": 256099, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N15_M07_F04_KI01_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_10_channel2.wav", "length": 256083, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_13_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_5_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_1_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_13_channel2.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_2_channel3.wav", "length": 260927, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M07_F04_KA22_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M07_F04_KA22_8_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M07_F04_KA22_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_11_channel2.wav", "length": 256184, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_16_channel3.wav", "length": 256093, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N09_M07_F10_KA05_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N09_M07_F10_KA05_2_channel3.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N09_M07_F10_KA05_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_16_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_15_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_6_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_18_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_15_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_18_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_8_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N09_M07_F10_KI01_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N09_M07_F10_KI01_15_channel3.wav", "length": 256103, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N09_M07_F10_KI01_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_11_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_17_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M07_F04_KA22_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M07_F04_KA22_14_channel2.wav", "length": 255999, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M07_F04_KA22_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_19_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_17_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_10_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_7_channel3.wav", "length": 256094, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_13_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_11_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_7_channel2.wav", "length": 257119, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_14_channel3.wav", "length": 260586, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_19_channel2.wav", "length": 256092, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_6_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F10_KI18_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F10_KI18_18_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F10_KI18_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_16_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_3_channel3.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_6_channel2.wav", "length": 256074, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_18_channel3.wav", "length": 256082, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_1_channel3.wav", "length": 256070, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N15_M07_F04_KI08_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N15_M07_F04_KI08_2_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N15_M07_F04_KI08_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N15_M07_F04_KI01_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N15_M07_F04_KI01_2_channel2.wav", "length": 256942, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N15_M07_F04_KI01_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_6_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_12_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_15_channel3.wav", "length": 256096, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_10_channel2.wav", "length": 256078, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_19_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_13_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_14_channel2.wav", "length": 256097, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_19_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_18_channel2.wav", "length": 260797, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_2_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_16_channel2.wav", "length": 256087, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_12_channel2.wav", "length": 256287, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_10_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_14_channel3.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_10_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_5_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_2_channel3.wav", "length": 259003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_2_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_11_channel2.wav", "length": 257449, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N15_M07_F04_KA30_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N15_M07_F04_KA30_5_channel2.wav", "length": 256084, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N15_M07_F04_KA30_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_20_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_2_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_1_channel3.wav", "length": 277460, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_1_channel3.wav", "length": 263829, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_16_channel2.wav", "length": 250604, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_5_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_13_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_17_channel3.wav", "length": 256005, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_8_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_4_channel3.wav", "length": 255999, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_2_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M07_F10_KA01_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M07_F10_KA01_3_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M07_F10_KA01_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N09_M07_F10_K004_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N09_M07_F10_K004_4_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N09_M07_F10_K004_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_8_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_18_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_1_channel2.wav", "length": 256083, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_12_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_15_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_4_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_4_channel3.wav", "length": 257468, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N09_M07_F10_KA30_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N09_M07_F10_KA30_13_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N09_M07_F10_KA30_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_12_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_5_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_2_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_12_channel3.wav", "length": 256171, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_20_channel2.wav", "length": 256090, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N09_M07_F10_KA05_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N09_M07_F10_KA05_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N09_M07_F10_KA05_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_13_channel3.wav", "length": 256091, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_13_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_3_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_19_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
+{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_18_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
diff --git a/process/MHB.png b/process/MHB.png
new file mode 100644
index 0000000000000000000000000000000000000000..7dc81ce482aa0adf31a3f90b56642fc7d5f68d48
--- /dev/null
+++ b/process/MHB.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af4a0505c6dfbd27e447b508399179e77249d3afa033da1ce58fa05df2319857
+size 2539379
diff --git a/process/map_deepship.py b/process/map_deepship.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3284cc5876dd38f3bb5df646b476564ff77d529
--- /dev/null
+++ b/process/map_deepship.py
@@ -0,0 +1,45 @@
+
+import argparse
+import json
+import shutil
+from pathlib import Path
+
+
+MHB_ROOT = Path(__file__).resolve().parent.parent
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("test_dir", type=Path)
+    args = parser.parse_args()
+
+    test_dir = args.test_dir.expanduser().resolve()
+    assert test_dir.is_dir(), f"Not found: {test_dir}"
+
+    count = 0
+    with (MHB_ROOT / "meta_data.jsonl").open("r", encoding="utf-8") as f:
+        for line in f:
+            if not line.strip():
+                continue
+
+            record = json.loads(line)
+            if record["task_name"] != "DeepShip":
+                continue
+
+            category, event, recording, segment = record["id"].split("_")
+            src = test_dir / category / event / f"{recording}.wav" / f"{segment}.wav"
+            dst = MHB_ROOT / record["input_ts"]["path"]
+
+            assert src.is_file(), f"Missing DeepShip segment: {src}"
+            assert dst.name == f"{record['id']}.wav"
+
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copy2(src, dst)
+            count += 1
+
+    assert count == 527, f"Expected 527, got {count}"
+    print(f"DeepShip: copied {count} files")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/process/map_iquique.py b/process/map_iquique.py
new file mode 100644
index 0000000000000000000000000000000000000000..72c8a6d268d803d914ced885161513037f6f4bfd
--- /dev/null
+++ b/process/map_iquique.py
@@ -0,0 +1,60 @@
+
+import argparse
+import csv
+import json
+from pathlib import Path
+
+import h5py
+import numpy as np
+
+
+MHB_ROOT = Path(__file__).resolve().parent.parent
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("data_dir", type=Path)
+    args = parser.parse_args()
+
+    data_dir = args.data_dir.expanduser().resolve()
+    assert (data_dir / "waveforms.hdf5").is_file()
+    assert (data_dir / "metadata.csv").is_file()
+
+    with (data_dir / "metadata.csv").open("r", encoding="utf-8") as f:
+        trace_names = {row["trace_name"] for row in csv.DictReader(f)}
+
+    count = 0
+    with h5py.File(data_dir / "waveforms.hdf5", "r") as h5, \
+         (MHB_ROOT / "meta_data.jsonl").open("r", encoding="utf-8") as f:
+        for line in f:
+            if not line.strip():
+                continue
+            record = json.loads(line)
+            if record["task_name"] != "Iquique":
+                continue
+
+            trace_name = record["meta_data"]["trace_name"]
+            assert trace_name in trace_names, f"Missing trace: {trace_name}"
+
+            bucket, position = trace_name.split("$")
+            index, channels, length = position.split(",")
+            index, channels, length = int(index), int(channels[1:]), int(length[1:])
+
+            assert channels == 3 and length == record["input_ts"]["length"]
+            assert record["id"] == f"{bucket.removeprefix('bucket')}_{index}_{channels}_{length}"
+
+            signal = h5["data"][bucket][index, :channels, :length].T.astype(np.float32)
+            assert signal.shape == (length, channels)
+
+            dst = MHB_ROOT / record["input_ts"]["path"]
+            assert dst.name == f"{record['id']}.npy"
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            np.save(dst, signal)
+            count += 1
+
+    assert count == 502, f"Expected 502, got {count}"
+    print(f"Iquique: created {count} NPY files")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/process/map_shipsear.py b/process/map_shipsear.py
new file mode 100644
index 0000000000000000000000000000000000000000..71ea7d6c353458f57e70bc72896842cdd8ddbab4
--- /dev/null
+++ b/process/map_shipsear.py
@@ -0,0 +1,65 @@
+
+import argparse
+import json
+from pathlib import Path
+
+import soundfile as sf
+
+
+MHB_ROOT = Path(__file__).resolve().parent.parent
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("test_dir", type=Path)
+    args = parser.parse_args()
+
+    test_dir = args.test_dir.expanduser().resolve()
+    assert test_dir.is_dir(), f"Not found: {test_dir}"
+
+    count = 0
+    with (MHB_ROOT / "meta_data.jsonl").open("r", encoding="utf-8") as f:
+        for line in f:
+            if not line.strip():
+                continue
+
+            record = json.loads(line)
+            if record["task_name"] != "ShipsEar":
+                continue
+
+            name = record["meta_data"]["ori_file_path"]
+
+            # Trawler 使用划分脚本生成的独立测试片段
+            if name.startswith("28__"):
+                name = Path(name).stem + "_test.wav"
+
+            matches = list(test_dir.glob(f"*{name}"))
+            assert len(matches) == 1, f"Expected one source for {name}, found {len(matches)}"
+
+            length = record["input_ts"]["length"]
+            sr = record["input_ts"]["fs"]
+            index = record["meta_data"]["seg_index"]
+            assert length == sr * 30
+
+            # 按 30 秒切分
+            with sf.SoundFile(matches[0]) as audio:
+                assert audio.samplerate == sr and audio.channels == 1, f"{matches[0], audio.samplerate, audio.channels}"
+                start = index * (sr * 15)
+                assert start + length <= audio.frames, f"Short recording: {matches[0]}"
+                audio.seek(start)
+                segment = audio.read(length, dtype="float32")
+
+            # 保存到 meta_data.jsonl 指定的路径
+            dst = MHB_ROOT / record["input_ts"]["path"]
+            assert dst.name == f"{record['id']}.wav"
+            dst.parent.mkdir(parents=True, exist_ok=True)
+
+            sf.write(dst, segment, sr, subtype="FLOAT")
+            count += 1
+
+    assert count == 84, f"Expected 84 segments, got {count}"
+    print(f"ShipsEar: created {count} WAV files")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/process/map_yaseen.py b/process/map_yaseen.py
new file mode 100644
index 0000000000000000000000000000000000000000..31ed263966f3bf2814da51b81b1955af500dd09d
--- /dev/null
+++ b/process/map_yaseen.py
@@ -0,0 +1,44 @@
+
+import argparse
+import json
+import shutil
+from pathlib import Path
+
+
+MHB_ROOT = Path(__file__).resolve().parent.parent
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("data_dir", type=Path)
+    args = parser.parse_args()
+
+    data_dir = args.data_dir.expanduser().resolve()
+    assert data_dir.is_dir(), f"Not found: {data_dir}"
+
+    count = 0
+    with (MHB_ROOT / "meta_data.jsonl").open("r", encoding="utf-8") as f:
+        for line in f:
+            if not line.strip():
+                continue
+            record = json.loads(line)
+            if record["task_name"] != "Yaseen":
+                continue
+
+            name = f"{record['id']}.wav"
+            category = record["id"].split("_")[1]
+            src = data_dir / f"{category}_New_3주기" / name
+            dst = MHB_ROOT / record["input_ts"]["path"]
+
+            assert src.is_file(), f"Missing: {src}"
+            assert dst.name == name
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copy2(src, dst)
+            count += 1
+
+    assert count == 200, f"Expected 200, got {count}"
+    print(f"Yaseen: copied {count} WAV files")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/process/process_raw2wav.py b/process/process_raw2wav.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6c433bcac17aacea6aadd7de1230b85affde796
--- /dev/null
+++ b/process/process_raw2wav.py
@@ -0,0 +1,74 @@
+
+# -*- coding: utf-8 -*-
+import json
+import wave
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+ROOT = Path(__file__).resolve().parent.parent
+CONVERT_TASKS = {"GWOSC", "LEAVES", "Iquique", "Llaima", "RadSeg", "RadioML"}
+
+
+def main():
+    total = converted = 0
+
+    with (ROOT / "meta_data.jsonl").open("r", encoding="utf-8") as fin, \
+         (ROOT / "wav_data.jsonl").open("w", encoding="utf-8") as fout:
+        for line in fin:
+            if not line.strip():
+                continue
+            data = json.loads(line)
+
+            if data["task_name"] in CONVERT_TASKS:
+                data_type = data["data_type"]
+                assert data_type in ("csv", "npy")
+
+                src_rel = Path(data["input_ts"]["path"])
+                src_path = ROOT / src_rel
+                assert src_rel.parent.name == "raw_input" and src_path.is_file()
+
+                if data_type == "csv":
+                    signal = pd.read_csv(src_path, header=None).values
+                else:
+                    signal = np.load(src_path)
+
+                signal = signal.flatten().astype(np.float64)
+                assert signal.size > 0
+                abs_max = np.max(np.abs(signal))
+                if abs_max > 0:
+                    signal = signal / abs_max
+                signal = (signal * 32767).astype(np.int16)
+
+                wav_rel = src_rel.parent.parent / "wav_input" / (src_rel.stem + ".wav")
+                wav_path = ROOT / wav_rel
+                wav_path.parent.mkdir(parents=True, exist_ok=True)
+
+                with wave.open(str(wav_path), "wb") as wf:
+                    wf.setnchannels(1)
+                    wf.setsampwidth(2)
+                    wf.setframerate(16000)
+                    wf.writeframes(signal.tobytes())
+
+                data["data_type"] = "wav"
+                data["input_ts"]["path"] = wav_rel.as_posix()
+                data["input_ts"]["length"] = int(signal.size)
+                data["input_ts"]["num_channel"] = 1
+                data["input_ts"]["fs"] = 16000
+
+                if data["task_name"] in ("Iquique", "RadSeg", "RadioML"):
+                    data["input_ts"]["channel_detail"] = []
+
+                converted += 1
+
+            fout.write(json.dumps(data, ensure_ascii=False) + "\n")
+            total += 1
+
+    assert total == 6128 and converted == 3428
+    print(f"Done: {converted} WAV files, {total} JSONL records")
+
+
+if __name__ == "__main__":
+    main()