chore: sync metadata, drop original files

README.md

@@ -1,65 +1,70 @@
----
-license: cc-by-4.0
-task_categories:
-- time-series-forecasting
-tags:
-- classification
-- forecasting
-- anomaly-detection
-- TSLib
-- tsfile
-- modality:timeseries
-pretty_name: FaceDetection (TsFile)
-size_categories:
-- 1M<n<10M
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: FaceDetection_train.tsfile
-  - split: test
-    path: FaceDetection_test.tsfile
-modality:
-- timeseries
-language:
-- en
----
-
-# FaceDetection (TsFile)
-
-Apache TsFile version of the `FaceDetection` UEA classification subset of
-[`thuml/Time-Series-Library`](https://huggingface.co/datasets/thuml/Time-Series-Library).
-
-## Overview
-
-MEG brain-activity recordings; classify whether a face or scramble was shown.
-
-- **Train samples:** 5,890  •  **Test samples:** 3,524.
-- **Dimensions (channels):** 144  •  **Series length:** 62.
-- **Classes:** 2.
-
-Each sample is an independent multivariate series. TRAIN and TEST are stored as
-two separate TsFiles (`FaceDetection_train.tsfile` / `FaceDetection_test.tsfile`).
-
-## Schema (TsFile structure)
-
-- **sample_index** (TAG) — one device per sample. Query one sample with
-  `WHERE sample_index=0`.
-- **Time** (INT64) — within-sample position (0..61); a frame index, not a
-  wall-clock timestamp (the sktime `.ts` source has none).
-- **dim_0 … dim_143** (FIELD, FLOAT) — the 144 channels at each position.
-- **class_label** (FIELD, STRING) — the sample's class, repeated on every row.
-
-Converted from the sktime `.ts` format. Nothing is dropped: every dimension,
-time point, and label is preserved.
-
-## Usage
-
-Read the `.tsfile` files with the Apache TsFile Java or Python SDK.
-
-## Source & license
-
-- Original dataset: https://huggingface.co/datasets/thuml/Time-Series-Library (subset `FaceDetection`)
-- Author / publisher: thuml (Tsinghua University)
-- Paper: https://arxiv.org/abs/2407.13278
-- License: CC BY 4.0
+---
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+tags:
+- time-series
+- forecasting
+- anomaly-detection
+- classification
+- TSLib
+- tsfile
+- modality:timeseries
+- timeseries
+- format:tsfile
+pretty_name: FaceDetection (TsFile)
+size_categories:
+- 1M<n<10M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: FaceDetection_train.tsfile
+  - split: test
+    path: FaceDetection_test.tsfile
+modality:
+- timeseries
+- tabular
+- text
+language:
+- en
+---
+
+# FaceDetection (TsFile)
+
+Apache TsFile version of the `FaceDetection` UEA classification subset of
+[`thuml/Time-Series-Library`](https://huggingface.co/datasets/thuml/Time-Series-Library).
+
+## Overview
+
+MEG brain-activity recordings; classify whether a face or scramble was shown.
+
+- **Train samples:** 5,890  •  **Test samples:** 3,524.
+- **Dimensions (channels):** 144  •  **Series length:** 62.
+- **Classes:** 2.
+
+Each sample is an independent multivariate series. TRAIN and TEST are stored as
+two separate TsFiles (`FaceDetection_train.tsfile` / `FaceDetection_test.tsfile`).
+
+## Schema (TsFile structure)
+
+- **sample_index** (TAG) — one device per sample. Query one sample with
+  `WHERE sample_index=0`.
+- **Time** (INT64) — within-sample position (0..61); a frame index, not a
+  wall-clock timestamp (the sktime `.ts` source has none).
+- **dim_0 … dim_143** (FIELD, FLOAT) — the 144 channels at each position.
+- **class_label** (FIELD, STRING) — the sample's class, repeated on every row.
+
+Converted from the sktime `.ts` format. Nothing is dropped: every dimension,
+time point, and label is preserved.
+
+## Usage
+
+Read the `.tsfile` files with the Apache TsFile Java or Python SDK.
+
+## Source & license
+
+- Original dataset: https://huggingface.co/datasets/thuml/Time-Series-Library (subset `FaceDetection`)
+- Author / publisher: thuml (Tsinghua University)
+- Paper: https://arxiv.org/abs/2407.13278
+- License: CC BY 4.0