Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
tsfile.exceptions.FileOpenError: 28:
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
scan = self._scan_metadata(all_files)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
with self._open_reader(file) as reader:
~~~~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
return TsFileReader(file)
File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
Clean Test TsFile
This dataset is an Apache TsFile conversion of
sattgle/clean-test, a LeRobot v2.1 SO101 follower
robot-manipulation dataset for the clean task.
It contains numeric trajectories, timing, and episode/task tags. Videos remain
in the original Hugging Face repository.
Source Dataset and Attribution
- Original dataset:
sattgle/clean-test - Repository owner, uploader, and sole contributor:
Hugging Face user sattgle (
sattgle) - License: Apache-2.0
- Task: "clean"
- Robot:
so101_follower; LeRobot version:v2.1 - Split:
train; sampling rate: 30 fps - Scale: 50 episodes, 32,220 frames, 1 task
- Source shards: 50 Parquet files totaling 1,680,276 bytes
- Source frame layout:
data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet - Paper, external homepage, and completed citation: not provided by the source card.
The source metadata, all 50 Parquet shards, and episode metadata consistently describe this 50-episode dataset.
TsFile Dataset
- TsFile:
data/sattgle_clean_test.tsfile(718,233 bytes) - Table:
sattgle_clean_test - Rows: 32,220; episodes/devices: 50; tasks: 1
- Time precision: milliseconds
- TsFile/source-Parquet size ratio: 0.427
meta/is preserved from the source, with onlymeta/info.jsonrewritten to describe the TsFile schema and source-video alignment.
Schema and Mapping
Time = round(timestamp * 1000) milliseconds. Time starts at zero and is
strictly increasing inside every episode. The source timestamp is dropped
afterward because it is represented by Time / 1000 seconds at the selected
precision.
| TsFile column | Role | Type | Source mapping |
|---|---|---|---|
Time |
TIME | TIMESTAMP | round(timestamp * 1000) ms |
episode_index |
TAG | STRING | Original INT64 episode index |
task_index |
TAG | STRING | Original INT64 task index |
frame_index |
FIELD | INT64 | Preserved |
sample_index |
FIELD | INT64 | Renamed from index |
action_0 ... action_5 |
FIELD | FLOAT | Flattened from action[6] |
observation_state_0 ... observation_state_5 |
FIELD | FLOAT | Flattened from observation.state[6] |
The six vector dimensions are shoulder_pan.pos, shoulder_lift.pos,
elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, and gripper.pos.
Dots in source names are replaced by underscores. No numeric row, episode,
task, state dimension, or action dimension is dropped.
Encodings and Compression
- FLOAT/DOUBLE: GORILLA + LZ4
- INT32/INT64: TS_2DIFF + LZ4
- Time: TS_2DIFF + LZ4
- BOOLEAN: RLE + LZ4 (the source contains no BOOLEAN field)
- TAG: TsFile table/device TAG storage
The physical table schema, every field codec, TAG roles, and all 32,220 rows were read back with the Apache TsFile Java API.
Videos
Videos are not included in this TsFile repository. The source contains 100 frame-aligned AV1 MP4 files (597,891,922 bytes), 640x360 at 30 fps with no audio, in two streams with 50 episode files each:
The source layout is
videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4.
Use episode_index, frame_index, and meta/episodes.jsonl to align numeric
rows with the original video frames.
Quality Checks
Source Parquet, staged Parquet, and complete Java TsFile readback all contain 32,220 rows. Scalar values, all flattened vector values, TAGs, episode indexes, Time mapping and monotonicity, physical codecs, size, and SHA-256 were checked locally. Conversion scripts and local quality-check reports are intentionally excluded from this upload-ready directory.
Minimal Read Example
from tsfile import TsFileReader
reader = TsFileReader("data/sattgle_clean_test.tsfile")
with reader.query_table(
"sattgle_clean_test",
["episode_index", "task_index", "frame_index", "sample_index",
"action_0", "observation_state_0"],
batch_size=65536,
) as result:
batch = result.read_arrow_batch()
print(batch.to_pandas().head())
reader.close()
Citation
The source card provides no personal name, paper, external homepage, or completed citation. Cite the original
Hugging Face dataset and Hugging Face user sattgle (sattgle) when using this conversion.
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