The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: ValueError
Message: Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 646, in get_module
patterns = sanitize_patterns(next(iter(metadata_configs.values()))["data_files"])
File "/usr/local/lib/python3.14/site-packages/datasets/data_files.py", line 151, in sanitize_patterns
raise ValueError(f"Some splits are duplicated in data_files: {splits}")
ValueError: Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train']Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
mug_hang_mp_500 (TsFile)
This repository contains a TsFile conversion of the Hugging Face dataset younghyopark/mug_hang_mp_500, a LeRobot-format robotics dataset created with LeRobot.
Modalities: Time-series.
Source Dataset
- Original dataset:
younghyopark/mug_hang_mp_500 - License:
apache-2.0 - Robot type:
DualPanda - Codebase version:
v2.1 - Episodes: 500
- Frames: 591,679
- Tasks: 1
- FPS: 50
- Original data path pattern:
data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet - Original video path:
None; total videos: 0
Converted Files
Converted rows: 591,679
TsFile shards: 6
Total TsFile size: 939,574,292 bytes
Table name:
mug_hang_mp_500Converted data path pattern recorded in
meta/info.json:data/mug_hang_mp_500_*.tsfiledata/mug_hang_mp_500_1.tsfile(158,338,984 bytes)data/mug_hang_mp_500_2.tsfile(158,768,860 bytes)data/mug_hang_mp_500_3.tsfile(159,295,248 bytes)data/mug_hang_mp_500_4.tsfile(159,249,339 bytes)data/mug_hang_mp_500_5.tsfile(158,149,388 bytes)data/mug_hang_mp_500_6.tsfile(145,772,473 bytes)
TsFile Schema Design
Timeis the TsFile time column in millisecond precision.episode_indexandtask_indexare TAG columns. They identify the LeRobot episode and task.- FIELD columns: 652 scalar measurements after flattening vector and matrix features.
- The original
timestampfield is dropped becauseTime = round(timestamp * 1000)and therefore represents the same value in milliseconds. - The original
indexcolumn is renamed tosample_index;frame_indexis retained. - Source feature names containing
.are converted to_in TsFile field names.
Flattening examples
| Source feature | Converted field range | Count |
|---|---|---|
observation.joint |
observation_joint_0 ... observation_joint_15 |
16 |
observation.state |
observation_state_0 ... observation_state_29 |
30 |
observation.environment_state |
observation_environment_state_0 ... observation_environment_state_13 |
14 |
observation.ee_pose |
observation_ee_pose_0 ... observation_ee_pose_15 |
16 |
action.ctrl |
action_ctrl_0 ... action_ctrl_15 |
16 |
action.joint |
action_joint_0 ... action_joint_15 |
16 |
action.ee_pose |
action_ee_pose_0 ... action_ee_pose_15 |
16 |
Array2D-style matrix features are flattened in row-major order. For example, a 3x3 matrix becomes 9 scalar FLOAT fields with suffixes _0 through _8.
Metadata
The source meta/ directory is mirrored under meta/. The file meta/info.json has been rewritten so that:
data_pathpoints todata/mug_hang_mp_500_*.tsfileinstead of the original Parquet pattern.featuresdescribes the converted TsFile scalar schema and roles (TIME,TAG,FIELD).tsfile_conversionrecords the source dataset, time mapping, row count, flattened features, renamed fields, and dropped fields.
Reading
from tsfile import TsFileReader
reader = TsFileReader("data/mug_hang_mp_500_1.tsfile")
# Inspect available table schemas or query table data with the Apache TsFile Python SDK.
print(reader.get_all_table_schemas())
reader.close()
Conversion Notes
Conversion notes:
- LeRobot-style robot dataset converted to TsFile via the generic
lerobotconverter. - Source metadata declares
robot_type=DualPanda, 500 episodes, 591,679 frames, 1 task, 50 fps, and Apache-2.0 license. - Numeric frame data is stored as one logical TsFile table under
data/. Because this dataset is wide, local conversion uses bounded Parquet row groups and may emitdata/mug_hang_mp_500_*.tsfileshards. - Source columns
episode_indexandtask_indexare TAG columns; no syntheticepisode_idortask_idcolumns are created. Query a single episode withWHERE episode_index=N. - Vector columns preserve the full source column name when flattened:
.is replaced with_and the element index is appended. Values are stored as single-precision FLOAT fields. - Time is synthesized as
round(timestamp * 1000)milliseconds. The sourcetimestampcolumn is not retained as a separate field because it equalsTime / 1000seconds;frame_indexis kept. - Source metadata has
total_videos=0andvideo_path=null; no videos are included in this converted repository. meta/is mirrored from the source except thatmeta/info.jsondescribes the converted TsFile schema and records the original video path separately.
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