Datasets:

azithromycin
/

justintiensmith_MT_State_Recognition_200

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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MT State Recognition 200 TsFile

This repository is a compact Apache TsFile conversion of justintiensmith/MT_State_Recognition_200, a LeRobot v3 state-recognition/manipulation dataset.

Source dataset and attribution

Original dataset: justintiensmith/MT_State_Recognition_200
Original author/uploader: Justin Tien-Smith
License: Apache-2.0
Task: state-recognition demonstrations that place cups or a glass spice container into a bowl.
Source card lineage: derived from MT_State_Recognition_200_OLD; 40 checklist-selected episodes were replaced by episodes 0-39 from MT_State_Recognition_40.
Paper/citation: the source card does not specify a paper or BibTeX citation.

Task index mapping:

0: Put a yellow-white striped cup in the bowl.
1: Put a brown cup in the bowl.
2: Put a white cup in the bowl.
3: Pick up the brown cup and place it into the bowl.
4: Pick up a glass spice container and place it into the bowl.
5: Place a white cup in the bowl.
6: Place a yellow-white striped cup in the bowl.
7: Place a brown cup in the bowl.
8: Place a glass spice container in the bowl.

Dataset scale and files

Split: train (0:200)
Episodes: 200
Rows/frames: 102,000
Tasks: 9
Sampling rate: 30 fps
Source Parquet shards: 1 (data/chunk-000/file-000.parquet)
Source row bytes: 4,897,269
Converted TsFile: data/justintiensmith_mt_state_recognition_200.tsfile (2,240,355 bytes; 0.457x source Parquet bytes)
Table: justintiensmith_mt_state_recognition_200

The source video layout is videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4 with five streams: observation.images.middle, observation.images.above, observation.images.right, observation.images.left, and observation.images.wrist. Videos are not copied into this TsFile repository; use the original Hugging Face videos/ tree. Frame/video alignment is preserved through episode_index, frame_index, and meta/episodes/ offsets.

TsFile schema

Time is round(timestamp * 1000) in milliseconds and restarts at zero for each episode. The source timestamp is dropped because it equals Time / 1000 seconds.

Column group	TsFile columns	Role/type
Time	`Time`	TIME, INT64 milliseconds
Device metadata	`episode_index`, `task_index`	TAG columns (TsFile table/device mechanism)
Scalar fields	`frame_index`, `sample_index`	FIELD, INT64; `sample_index` is source `index` renamed
Action	`action_0` ... `action_5`	FIELD, FLOAT32; flattened from `action[6]`
Observation state	`observation_state_0` ... `observation_state_5`	FIELD, FLOAT32; flattened from `observation.state[6]` (dot replaced by underscore)

No numeric rows or action/state dimensions are dropped. Video tensors are omitted because TsFile output is numeric time-series data.

Encoding and compression

The writer applies the requested compact profile: FLOAT/DOUBLE = GORILLA + LZ4; INT32/INT64 and Time = TS_2DIFF + LZ4; BOOLEAN (if present) = RLE + LZ4; TAG values use TsFile table/device storage. The resulting schema and row count were read back locally and compared with the staged Parquet.

Videos

Videos are not duplicated in this converted repository. The original Hugging Face source retains all five streams under videos/ using the path template above. The numeric rows remain aligned to source video frames using episode_index, frame_index and meta/episodes/ offsets.

Validation

The local validation report is kept outside the upload set under the source dataset's .conversion_workdir/reports/ directory. It records staged/converted row counts, schema, per-episode Time checks, and size comparison. Conversion scripts and reports are intentionally local and are not part of the TsFile upload set.

Minimal read example

from tsfile import TsFileReader

reader = TsFileReader("data/justintiensmith_mt_state_recognition_200.tsfile")
table = reader.get_all_table_schemas()["justintiensmith_mt_state_recognition_200"]
fields = [c.get_column_name() for c in table.get_columns() if c.get_column_name() != "Time"]
with reader.query_table("justintiensmith_mt_state_recognition_200", fields, batch_size=65536) as result:
    batch = result.read_arrow_batch()

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Total file size:

10.3 MB