chore: sync metadata, drop original files

README.md

@@ -1,62 +1,67 @@
----
-license: cc-by-4.0
-task_categories:
-- time-series-forecasting
-tags:
-- anomaly-detection
-- forecasting
-- classification
-- TSLib
-- tsfile
-- modality:timeseries
-pretty_name: SMAP (TsFile)
-size_categories:
-- 1M<n<10M
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: SMAP_train.tsfile
-  - split: test
-    path: SMAP_test.tsfile
-modality:
-- timeseries
-language:
-- en
----
-
-# SMAP (TsFile)
-
-Apache TsFile version of the `SMAP` anomaly-detection subset of
-[`thuml/Time-Series-Library`](https://huggingface.co/datasets/thuml/Time-Series-Library).
-
-## Overview
-
-Soil Moisture Active Passive satellite telemetry (25 channels) for anomaly detection.
-
-- **Train:** 135,183 rows (all normal).
-- **Test:** 427,617 rows (with per-timestep 0/1 anomaly labels).
-- **Channels:** 25.
-
-The train and test segments are stored as two separate TsFiles
-(`SMAP_train.tsfile` / `SMAP_test.tsfile`), preserving the original split.
-
-## Schema (TsFile structure)
-
-- **Time** (INT64, milliseconds) — row index * 1000 ms (the `.npy` source has no timestamp; a monotone 1 Hz proxy axis).
-- **FIELD** (25 channels, FLOAT) — the sensor/metric channels.
-- **label** (INT32) — per-timestep anomaly flag (0/1). The train file is all 0
-  (no ground-truth labels); the test file carries the anomaly labels.
-
-No channels or rows are dropped.
-
-## 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 `SMAP`)
-- 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: SMAP (TsFile)
+size_categories:
+- 1M<n<10M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: SMAP_train.tsfile
+  - split: test
+    path: SMAP_test.tsfile
+modality:
+- timeseries
+- tabular
+- text
+language:
+- en
+---
+
+# SMAP (TsFile)
+
+Apache TsFile version of the `SMAP` anomaly-detection subset of
+[`thuml/Time-Series-Library`](https://huggingface.co/datasets/thuml/Time-Series-Library).
+
+## Overview
+
+Soil Moisture Active Passive satellite telemetry (25 channels) for anomaly detection.
+
+- **Train:** 135,183 rows (all normal).
+- **Test:** 427,617 rows (with per-timestep 0/1 anomaly labels).
+- **Channels:** 25.
+
+The train and test segments are stored as two separate TsFiles
+(`SMAP_train.tsfile` / `SMAP_test.tsfile`), preserving the original split.
+
+## Schema (TsFile structure)
+
+- **Time** (INT64, milliseconds) — row index * 1000 ms (the `.npy` source has no timestamp; a monotone 1 Hz proxy axis).
+- **FIELD** (25 channels, FLOAT) — the sensor/metric channels.
+- **label** (INT32) — per-timestep anomaly flag (0/1). The train file is all 0
+  (no ground-truth labels); the test file carries the anomaly labels.
+
+No channels or rows are dropped.
+
+## 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 `SMAP`)
+- Author / publisher: thuml (Tsinghua University)
+- Paper: https://arxiv.org/abs/2407.13278
+- License: CC BY 4.0