| --- |
| pretty_name: Lapras Reasoning Datasets |
| license: other |
| language: |
| - en |
| task_categories: |
| - question-answering |
| - text-generation |
| tags: |
| - time-series |
| - reasoning |
| - chain-of-thought |
| - latent-reasoning |
| - ecg |
| - sleep-staging |
| - human-activity-recognition |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: ecg |
| data_files: |
| - split: train |
| path: ecg/train.jsonl |
| - split: test |
| path: ecg/test.jsonl |
| - config_name: ecg_ts_tags |
| data_files: |
| - split: train |
| path: ecg/train_with_ts_tags.jsonl |
| - split: test |
| path: ecg/test_with_ts_tags.jsonl |
| - config_name: sleep |
| data_files: |
| - split: train |
| path: sleep/train.jsonl |
| - split: test |
| path: sleep/test.jsonl |
| - config_name: sleep_ts_tags |
| data_files: |
| - split: train |
| path: sleep/train_with_ts_tags.jsonl |
| - split: test |
| path: sleep/test_with_ts_tags.jsonl |
| - config_name: har |
| data_files: |
| - split: train |
| path: har/train.jsonl |
| - split: test |
| path: har/test.jsonl |
| - config_name: har_ts_tags |
| data_files: |
| - split: train |
| path: har/train_with_ts_tags.jsonl |
| - split: test |
| path: har/test_with_ts_tags.jsonl |
| - config_name: tsr |
| data_files: |
| - split: train |
| path: tsr/train.jsonl |
| - split: test |
| path: tsr/test.jsonl |
| - config_name: tsr_ts_tags |
| data_files: |
| - split: train |
| path: tsr/train_with_ts_tags.jsonl |
| - split: test |
| path: tsr/test_with_ts_tags.jsonl |
| - config_name: engine |
| data_files: |
| - split: train |
| path: engine/train.jsonl |
| - split: test |
| path: engine/test.jsonl |
| - config_name: engine_ts_tags |
| data_files: |
| - split: train |
| path: engine/train_with_ts_tags.jsonl |
| - split: test |
| path: engine/test_with_ts_tags.jsonl |
| --- |
| |
| # Dataset Card for Lapras Reasoning Datasets |
|
|
| Five time series question answering datasets with reference reasoning traces, used to train and evaluate |
| **Lapras** (latent reasoning for time series language models) and its No-CoT, CoT, iCoT and COCONUT baselines. |
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| Each example pairs a natural-language question with a multichannel time series and a reference reasoning |
| trace that ends in `Answer: <label>`. The five tasks cover cardiological diagnosis (ECG), sleep-stage |
| classification (Sleep), human activity recognition (HAR), counterfactual consequence prediction (TSR) and |
| aero-engine fault diagnosis (Engine). Every task ships in two prompt variants with identical rows: a plain |
| prompt, and a `*_with_ts_tags` prompt with one `<name> (length L): <ts><ts/>` placeholder line per channel. |
|
|
| - **Curated by:** the Lapras authors (Yuliang Chen et al., see [Citation](#citation)) |
| - **Language(s) (NLP):** English |
| - **License:** per subset (see [Data Sources](#data-sources)) |
|
|
| ### Links |
|
|
| - **Repository:** https://github.com/yuc0805/Lapras |
| - **Paper:** https://arxiv.org/abs/2610.11111 |
| - **Checkpoints:** https://huggingface.co/leochen085/Lapras |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| - Supervised finetuning and evaluation of time series language models, with or without reasoning traces. |
| - Reproducing the Lapras experiments: download into the `dataset/` folder of the code repository. |
|
|
| ```bash |
| huggingface-cli download leochen085/Lapras-Reasoning-Datasets --repo-type dataset --include "*.jsonl" --local-dir dataset |
| ``` |
|
|
| ### Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("leochen085/Lapras-Reasoning-Datasets", "har") |
| print(ds) |
| ``` |
|
|
| ``` |
| DatasetDict({ |
| train: Dataset({ |
| features: ['input', 'timeseries', 'output'], |
| num_rows: 68542 |
| }) |
| test: Dataset({ |
| features: ['input', 'timeseries', 'output'], |
| num_rows: 8222 |
| }) |
| }) |
| ``` |
|
|
| Each row is a dict with `input` (str), `timeseries` (list of channels, each a list of floats) and `output` (str). |
|
|
| ### Processor, collator and DataLoader |
|
|
| [`lapras_dataloader.py`](lapras_dataloader.py) holds a standard multimodal processor and collator, as for |
| vision-language models: the processor tokenizes the text and packs the time series into a tensor, and the |
| collator builds training batches in which only the response is supervised. |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| hf_hub_download("leochen085/Lapras-Reasoning-Datasets", "lapras_dataloader.py", repo_type="dataset", local_dir=".") |
| |
| from torch.utils.data import DataLoader |
| from transformers import AutoTokenizer |
| from lapras_dataloader import TimeSeriesProcessor, TimeSeriesCollator |
| |
| processor = TimeSeriesProcessor(AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")) |
| loader = DataLoader(ds["train"], batch_size=4, shuffle=True, collate_fn=TimeSeriesCollator(processor)) |
| batch = next(iter(loader)) |
| ``` |
|
|
| ``` |
| input_ids (4, 514) torch.int64 # length varies by batch |
| attention_mask (4, 514) torch.int64 |
| timeseries (4, 3, 128) torch.float32 # (batch, channels, length), zero-padded |
| timeseries_mask (4, 3, 128) torch.bool # True on real samples |
| labels (4, 514) torch.int64 # -100 on the prompt and padding |
| ``` |
|
|
| Series of different lengths (TSR) are zero-padded to the longest in the batch and marked in `timeseries_mask`. |
|
|
| ### Decoding back |
|
|
| ```python |
| tok = processor.tokenizer |
| |
| ids = batch["input_ids"][0][batch["attention_mask"][0].bool()] |
| tok.decode(ids) |
| # '<|im_start|>user\nYou are given accelerometer data in all three dimensions. ... <|eot|>\nAnswer: sitting<|im_end|>' |
| |
| target = tok.decode(batch["labels"][0][batch["labels"][0] != -100], skip_special_tokens=True) |
| target # the example's `output`, character for character |
| target.rsplit("Answer:", 1)[1].strip() # e.g. 'sitting' |
| |
| n_ch = batch["timeseries_mask"][0].any(-1).sum() |
| batch["timeseries"][0][batch["timeseries_mask"][0]].view(n_ch, -1).tolist() # the example's `timeseries` |
| ``` |
|
|
| ## Dataset Structure |
|
|
| ``` |
| ecg/ train.jsonl test.jsonl train_with_ts_tags.jsonl test_with_ts_tags.jsonl |
| sleep/ ... |
| har/ ... |
| tsr/ ... |
| engine/ ... |
| ``` |
|
|
| | Config | Task | Train | Test | Channels | Length | Answer | |
| |---|---|---:|---:|---:|---:|---| |
| | `ecg` | cardiological diagnosis | 5,800 | 643 | 12 | 1000 | `Yes` / `No` | |
| | `sleep` | sleep-stage classification | 7,413 | 923 | 1 | 1500 | `Wake`, `Non-REM stage 1`, `Non-REM stage 2`, `Non-REM stage 3`, `REM sleep` | |
| | `har` | human activity recognition | 68,542 | 8,222 | 3 | 128 | `biking`, `lying`, `running`, `sitting`, `standing`, `walking`, `walking_down`, `walking_up` | |
| | `tsr` | counterfactual consequence prediction | 22,566 | 4,094 | 1 | 128–1020 | option letter + text (`A`–`D`) | |
| | `engine` | aero-engine fault diagnosis | 2,305 | 193 | 33 | 600 | option letter + text (`A`–`D`) | |
|
|
| ### Data Fields |
|
|
| - `input` (string): the prompt (question, instructions and, for multiple choice, the options). |
| - `timeseries` (list of list of float): one list per channel. ECG and Sleep series are z-normalized per |
| channel; HAR, TSR and Engine series keep the scale of their source release. |
| - `output` (string): the reference trace. Except for ECG, reasoning steps are delimited by `<|bot|>` and |
| `<|eot|>`; HAR, Sleep and TSR traces also contain segment-selection calls |
| `<timeseries_selection_tool> [channel, start, end] </timeseries_selection_tool>` (Sleep: `[start, end]`; |
| inclusive sample indices). ECG traces are a single paragraph. Every trace ends with `Answer: <label>`. |
| - `ecg` plain files only: `_uid` (identifier of the source ECG-Reasoning-Benchmark sample, prefixed |
| `mimic_iv_ecg|` or `ptbxl|`) and `original_trace` (the source's multi-stage annotation, JSON string). |
|
|
| The `*_with_ts_tags` files contain the same rows in the same order; only `input` differs, by the |
| placeholder lines prepended to the prompt. |
|
|
| ## Data Sources |
|
|
| | Subset | Source | |
| |---|---| |
| | ECG | [ECG-Reasoning-Benchmark](https://huggingface.co/datasets/Jwoo5/ECG-Reasoning-Benchmark) | |
| | Sleep | [OpenTSLM SleepEDF-CoT](https://github.com/StanfordBDHG/OpenTSLM) | |
| | HAR | [OpenTSLM HAR-CoT](https://github.com/StanfordBDHG/OpenTSLM) | |
| | TSR | [Merrill et al. (2024)](https://huggingface.co/datasets/mikeam/time-series-reasoning) | |
| | Engine | [EngineMT-QA](https://huggingface.co/datasets/pandalin98/EngineMT-QA) | |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{chen2026lapras, |
| title = {Lapras: Latent Reasoning for Time Series Language Models}, |
| author = {Chen, Yuliang and Wu, Yu Yvonne and Langer, Patrick and Pillai, Arvind and Regmi, Sudarshan and |
| Maritsch, Martin and Liu, Juncheng and Jakob, Robert and Kaar, Thomas and Griffin, Tess Z. and |
| Marsch, Lisa and Heinz, Michael V. and Jacobson, Nicholas C. and Campbell, Andrew}, |
| journal = {arXiv preprint arXiv:2610.11111}, |
| year = {2026} |
| } |
| ``` |
|
|
