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 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 Four Tasks TsFile

Apache TsFile edition of Mwuqiu/So100_Four_Tasks, a LeRobot v2.1 SO100 manipulation dataset. The numeric trajectories are stored in one table-model TsFile.

Source and attribution

  • Original author, repository owner, and uploader: WuQiu (Mwuqiu).
  • License: Apache-2.0.
  • The source card does not provide a homepage, paper, or completed BibTeX citation.
  • Split: train; 100 episodes; 44,599 frame rows; five tasks; 30 FPS; 100 source Parquet shards.
  • The repository name says Four_Tasks, while its current meta/info.json and meta/tasks.jsonl contain five tasks. The current metadata and Parquet data are used here.
Task index Task Episodes Rows
0 Put the red square sponge into the box 20 8,641
1 Put the green square sponge into the box 20 8,984
2 Put the yellow duck into the box 20 9,286
3 Put the black duck into the box 20 9,230
4 Put the blue square sponge into the box 20 8,458

Data layout

The table is mwuqiu_so100_four_tasks and contains 44,599 rows across 100 TAG devices. The source Parquet shards total 2,460,918 bytes; the TsFile is 864,985 bytes (35.1% of the source Parquet size).

Column TsFile role Type Meaning
Time TIME INT64 milliseconds round(timestamp * 1000), restarting at zero per episode
episode_index TAG STRING from source INT64 Source episode identity
task_index TAG STRING from source INT64 Source task identity
frame_index FIELD INT64 Frame position within the episode
sample_index FIELD INT64 Source index, renamed for clarity
action_0 ... action_5 FIELD FLOAT Flattened action[6]
observation_state_0 ... observation_state_5 FIELD FLOAT Flattened observation.state[6]

timestamp is not retained as a separate FIELD because it is represented by Time / 1000 seconds. The vector column names preserve their source prefixes, with dots changed to underscores. No trajectory row, episode, task, action dimension, or state dimension is removed.

The data/ directory contains only mwuqiu_so100_four_tasks.tsfile. Source metadata remains under meta/; source trajectory Parquet files are not placed in meta/.

Videos and alignment

The 200 source H.264 videos are not included here. They remain under videos/chunk-000 in the original repository:

The source template is videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4. Use episode_index and frame_index to align each numeric row with both 30 FPS video streams.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/mwuqiu_so100_four_tasks.tsfile")
with reader.query_table(
    "mwuqiu_so100_four_tasks",
    ["episode_index", "task_index", "frame_index", "sample_index", "action_0", "observation_state_0"],
    batch_size=1024,
) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()
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