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 68, 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.
SO100 Test TsFile
This dataset is an Apache TsFile conversion of
HarrisonLee24/so100_test, a LeRobot v2.1 SO100
robot-manipulation dataset for grasping a LEGO block and placing it in a bin.
It contains numeric trajectories, frame timing, and episode/task tags. Videos
remain in the original Hugging Face repository.
Source Dataset and Attribution
- Original dataset:
HarrisonLee24/so100_test - Original author, repository owner, uploader, and commit author:
Harrison Lee (
HarrisonLee24) - License: Apache-2.0
- Task: "Grasp a lego block and put it in the bin."
- Robot:
so100; LeRobot version:v2.1 - Split:
train; sampling rate: 30 fps - Scale: 92 episodes, 80,091 frame rows, 1 task
- Source shards: 92 Parquet files totaling 3,926,293 bytes
- Episode lengths: 868 to 873 frames
- 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 README embeds an older two-episode copy of meta/info.json. The
current meta/info.json, 92 source Parquet files, meta/episodes.jsonl, and
meta/episodes_stats.jsonl consistently describe the 92-episode dataset and
are used here.
Data Layout
- TsFile:
data/harrisonlee24_so100_test.tsfile(1,372,574 bytes) - Table:
harrisonlee24_so100_test - Rows: 80,091; episodes/devices: 92; tasks: 1
- Time precision: milliseconds
- Source metadata is retained under
meta/;meta/info.jsonpoints to the TsFile data path and records the field mapping 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 main_shoulder_pan, main_shoulder_lift,
main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper.
Dots in source names are replaced by underscores. No numeric row, episode,
task, state dimension, or action dimension is dropped.
Videos
Videos are not included in this TsFile dataset. Current meta/info.json
references 184 frame-aligned H.264 MP4 files (565,481,091 bytes), 640x480 at
30 fps without audio, in two streams with 92 episode files each:
observation.images.global_cam- 92 files, 363,719,494 bytesobservation.images.hand_eye_cam- 92 files, 201,761,597 bytes
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.
The repository also retains 65 files in five older video directories that are
not referenced by the current meta/info.json: action_cam (20), laptop
(10), osmo (5), phone (10), and pocket_cam (20). They remain accessible
under the source videos/ tree, but this card
does not claim frame alignment for those historical streams.
Minimal Read Example
from tsfile import TsFileReader
reader = TsFileReader("data/harrisonlee24_so100_test.tsfile")
with reader.query_table(
"harrisonlee24_so100_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 paper or completed citation. Cite the original
Hugging Face dataset and Harrison Lee (HarrisonLee24) when using this data.
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