Datasets:

azithromycin
/

masato-ka_so100_cutlery_handling_simple

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.

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SO100 Cutlery Handling Simple TsFile

This dataset is an Apache TsFile conversion of masato-ka/so100_cutlery_handling_simple (https://huggingface.co/datasets/masato-ka/so100_cutlery_handling_simple), a LeRobot v2.1 SO100 robot-manipulation dataset.

Modalities: Time-series. It contains numeric observations, actions, frame timing, episode/task tags, and mirrored source metadata. Camera videos remain in the original Hugging Face dataset.

Source Dataset and Author

Source dataset: masato-ka/so100_cutlery_handling_simple
Original author/uploader: Masato Kawamura (masato-ka)
Authorship evidence: the source repository and profile identify Masato Kawamura as creator/uploader; no paper or completed citation is supplied.
License: Apache-2.0; robot type: so100; LeRobot version: v2.1; split: train; sampling rate: 30 fps
Scale: 50 episodes, 29,853 frames, 1 task
Episode length: 593-599 frames (median 597)
Source frame layout: data/chunk-000/episode_{episode_index:06d}.parquet
Source numeric files: 50 Parquet files totaling 1,585,385 bytes
Source video layout: videos/chunk-000/observation.images.laptop/episode_{episode_index:06d}.mp4

TsFile Schema

Time = round(timestamp * 1000) milliseconds, restarting at zero per episode.

TsFile column	Type	Role	Source mapping
Time	INT64/TIMESTAMP	TIME	round(timestamp * 1000)
episode_index	STRING	TAG	Source episode_index
task_index	STRING	TAG	Source task_index
frame_index	INT64	FIELD	Preserved
sample_index	INT64	FIELD	Renamed from index
action_0 ... action_5	FLOAT	FIELD	Flattened from action[6]
observation_state_0 ... observation_state_5	FLOAT	FIELD	Flattened from observation.state[6]

The six dimensions are main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper.

Videos

Videos are not copied here. The pinned source contains 50 frame-aligned MP4 files (376,904,819 bytes, about 359.4 MiB) under https://huggingface.co/datasets/masato-ka/so100_cutlery_handling_simple/tree/0177bce04d2eafa26af7771cadc029e29d07aa13/videos/chunk-000/observation.images.laptop. The source metadata describes 640x480 AV1 at 30 fps without audio. episode_index, frame_index, and meta/episodes preserve alignment.

Usage

from tsfile import TsFileReader
reader = TsFileReader("data/masato_ka_so100_cutlery_handling_simple_train.tsfile")
with reader.query_table("masato_ka_so100_cutlery_handling_simple_train",
        ["episode_index", "task_index", "frame_index", "sample_index",
         "action_0", "observation_state_0"],
        batch_size=65536) as result:
    print(result.read_arrow_batch().to_pandas().head())

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