diff --git a/README.md b/README.md
index b54a70ea84f6f156f07ef91f27d1867543fac523..b9e9ed0394650a9fbac37fda2a1f4b3ddd3d7e4c 100644
--- a/README.md
+++ b/README.md
@@ -1,95 +1,98 @@
----
-pretty_name: eval1_chengming TsFile
-tags:
-- robotics
-- LeRobot
-- SO-101
-- imitation-learning
-- tsfile
-- modality:timeseries
-modality:
-- timeseries
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/eval1_chengming.tsfile
-size_categories:
-- 10K<n<100K
----
-
-# eval1_chengming
-
-This dataset was converted from
-[`robot-learning-group47/eval1_chengming`](https://huggingface.co/datasets/robot-learning-group47/eval1_chengming)
-to Apache TsFile format.
-
-Modalities: Time-series. The original dataset also contains a front-camera
-video stream, which remains in the source Hugging Face dataset and is not
-included in this converted repository.
-
-## Source Dataset
-
-- Original dataset: [`robot-learning-group47/eval1_chengming`](https://huggingface.co/datasets/robot-learning-group47/eval1_chengming)
-- Source format: LeRobot v3 parquet frames with metadata and one front-camera video stream
-- Robot type: `so_follower`
-- Split: `train`
-- Episodes: 60
-- Frames / TsFile rows: 13,408
-- Tasks: 3
-- Sampling rate: 15 fps
-- License metadata: not provided by the source dataset card
-- Original data path: `data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet`
-- Original video path: `videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4`
-
-The episode metadata names three task variants: put the banana in the red,
-green, or blue colored bowl.
-
-## Files
-
-- `data/eval1_chengming.tsfile`: converted numeric time-series table
-- `meta/`: source metadata mirrored from the original dataset, with
-  `meta/info.json` rewritten to describe the converted TsFile layout
-
-Videos are not included in this repository. The original camera video stream
-`observation.images.front` remains in the source dataset under
-[`videos/`](https://huggingface.co/datasets/robot-learning-group47/eval1_chengming/tree/main/videos).
-
-## Schema
-
-The TsFile table name is `eval1_chengming`.
-
-- `Time`: integer timestamp in milliseconds, synthesized as `round(timestamp * 1000)`
-- TAG columns: `episode_index`, `task_index`
-- FIELD columns: `frame_index`, `sample_index`, `action_0..action_5`,
-  `observation_state_0..observation_state_5`
-
-The six action and observation-state dimensions use the original LeRobot joint
-order: `shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`,
-`wrist_flex.pos`, `wrist_roll.pos`, and `gripper.pos`.
-
-## Conversion Notes
-
-- Converted with the generic `lerobot` converter in the HuggingFace-to-TsFile
-  pipeline.
-- All 60 episodes are stored in one TsFile. Use `episode_index` and
-  `task_index` as TAG filters when querying.
-- Source `action[6]` was flattened to `action_0..action_5` as FLOAT fields.
-- Source `observation.state[6]` was flattened to `observation_state_0..5` as
-  FLOAT fields.
-- Source `index` was renamed to `sample_index`.
-- Source `timestamp` is not retained as a separate field because the same
-  information is represented by `Time / 1000` seconds.
-- Source video field `observation.images.front` is omitted from this TsFile
-  conversion and remains available in the original dataset.
-- No rows were dropped.
-
-## Minimal Read Example
-
-```python
-from tsfile import TsFileReader
-
-reader = TsFileReader("data/eval1_chengming.tsfile")
-schemas = reader.get_all_table_schemas()
-print(schemas.keys())
-```
+---
+pretty_name: eval1_chengming TsFile
+tags:
+- robotics
+- LeRobot
+- SO-101
+- imitation-learning
+- tsfile
+- modality:timeseries
+- timeseries
+- format:tsfile
+modality:
+- tabular
+- timeseries
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/eval1_chengming.tsfile
+size_categories:
+- 10K<n<100K
+---
+
+# eval1_chengming
+
+This dataset was converted from
+[`robot-learning-group47/eval1_chengming`](https://huggingface.co/datasets/robot-learning-group47/eval1_chengming)
+to Apache TsFile format.
+
+Modalities: Time-series. The original dataset also contains a front-camera
+video stream, which remains in the source Hugging Face dataset and is not
+included in this converted repository.
+
+## Source Dataset
+
+- Original dataset: [`robot-learning-group47/eval1_chengming`](https://huggingface.co/datasets/robot-learning-group47/eval1_chengming)
+- Source format: LeRobot v3 parquet frames with metadata and one front-camera video stream
+- Robot type: `so_follower`
+- Split: `train`
+- Episodes: 60
+- Frames / TsFile rows: 13,408
+- Tasks: 3
+- Sampling rate: 15 fps
+- License metadata: not provided by the source dataset card
+- Original data path: `data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet`
+- Original video path: `videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4`
+
+The episode metadata names three task variants: put the banana in the red,
+green, or blue colored bowl.
+
+## Files
+
+- `data/eval1_chengming.tsfile`: converted numeric time-series table
+- `meta/`: source metadata mirrored from the original dataset, with
+  `meta/info.json` rewritten to describe the converted TsFile layout
+
+Videos are not included in this repository. The original camera video stream
+`observation.images.front` remains in the source dataset under
+[`videos/`](https://huggingface.co/datasets/robot-learning-group47/eval1_chengming/tree/main/videos).
+
+## Schema
+
+The TsFile table name is `eval1_chengming`.
+
+- `Time`: integer timestamp in milliseconds, synthesized as `round(timestamp * 1000)`
+- TAG columns: `episode_index`, `task_index`
+- FIELD columns: `frame_index`, `sample_index`, `action_0..action_5`,
+  `observation_state_0..observation_state_5`
+
+The six action and observation-state dimensions use the original LeRobot joint
+order: `shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`,
+`wrist_flex.pos`, `wrist_roll.pos`, and `gripper.pos`.
+
+## Conversion Notes
+
+- Converted with the generic `lerobot` converter in the HuggingFace-to-TsFile
+  pipeline.
+- All 60 episodes are stored in one TsFile. Use `episode_index` and
+  `task_index` as TAG filters when querying.
+- Source `action[6]` was flattened to `action_0..action_5` as FLOAT fields.
+- Source `observation.state[6]` was flattened to `observation_state_0..5` as
+  FLOAT fields.
+- Source `index` was renamed to `sample_index`.
+- Source `timestamp` is not retained as a separate field because the same
+  information is represented by `Time / 1000` seconds.
+- Source video field `observation.images.front` is omitted from this TsFile
+  conversion and remains available in the original dataset.
+- No rows were dropped.
+
+## Minimal Read Example
+
+```python
+from tsfile import TsFileReader
+
+reader = TsFileReader("data/eval1_chengming.tsfile")
+schemas = reader.get_all_table_schemas()
+print(schemas.keys())
+```
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@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c3297561fbe77b1159a9be1d8fe3a550c1bd860514fc2782c1cce5e29dd2e626
-size 2240