Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
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 66, 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.

SO-100 Sorting (TsFile)

This dataset is an Apache TsFile conversion of the Hugging Face dataset dragon-95/so100_sorting. The source dataset was created using LeRobot.

Modalities: Time-series. The original repository also contains synchronized video streams; videos are not included in this converted repository.

Source Dataset

  • Original dataset: dragon-95/so100_sorting
  • License: apache-2.0
  • LeRobot codebase version: v2.0
  • Robot type: so100
  • Task: Put the object in box A into box B
  • Split: train (0:61)
  • Source scale from meta/info.json: 61 episodes, 95,346 frames, 1 task
  • Source video count: 122
  • Sampling rate: 50 fps
  • Source data layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
  • Source video layout: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

Converted Files

  • TsFile: data/so100_sorting.tsfile
  • Converted rows: 95,346
  • TsFile table: so100_sorting
  • Time precision: milliseconds
  • TAG columns: episode_index, task_index

Schema

Time is synthesized as round(timestamp * 1000) in milliseconds. The source timestamp column is dropped because it is redundant with Time / 1000 seconds. At 50 fps, consecutive frames are spaced by about 20 ms.

TAG columns:

  • episode_index
  • task_index

FIELD columns:

  • frame_index
  • sample_index (renamed from source index)
  • action_0 to action_5
  • observation_state_0 to observation_state_5

Vector features are flattened by preserving the source feature name and replacing . with _. For example, observation.state becomes observation_state_0 to observation_state_5. The 6-element action and observation.state vectors use the source joint order: main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper.

Video Policy

The following source video features are not converted into TsFile and are not uploaded here:

  • observation.images.laptop
  • observation.images.phone

Use the original dataset for videos: dragon-95/so100_sorting/videos.

Metadata

The source meta/ files are mirrored in this repository. meta/info.json is updated so data_path points to data/so100_sorting.tsfile and includes a tsfile_conversion object documenting the Time mapping, TAG columns, flattened features, dropped fields, and video policy.

Validation

The converted TsFile was validated with the project pipeline and read back using the TsFile Python SDK:

  • staged Parquet rows: 95,346
  • TsFile metadata rows: 95,346
  • TsFile query rows: 95,346
  • TsFile size: 1,682,521 bytes

Usage

from tsfile import TsFileReader

path = "data/so100_sorting.tsfile"
with TsFileReader(path) as reader:
    schemas = reader.get_all_table_schemas()
    print(schemas.keys())
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