Datasets:

---

Gbone3176

/

orena-frame

Like 0

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
id: string
image: string
conversations: list<item: struct<from: string, value: string>>
  child 0, item: struct<from: string, value: string>
      child 0, from: string
      child 1, value: string
meta: struct<dataset: string, dataset_short: string, split: string, track: string, source_id: int64, video (... 357 chars omitted)
  child 0, dataset: string
  child 1, dataset_short: string
  child 2, split: string
  child 3, track: string
  child 4, source_id: int64
  child 5, video: string
  child 6, timestamp_start: string
  child 7, timestamp_end: string
  child 8, timestamp_sec: int64
  child 9, frame_index: int64
  child 10, video_frame_count: int64
  child 11, video_duration_sec: double
  child 12, procedure_type: string
  child 13, answer_format: string
  child 14, answer_format_norm: string
  child 15, generation: string
  child 16, clinical_relevance: bool
  child 17, ood: bool
  child 18, primary_capability: string
  child 19, secondary_capabilities: list<item: string>
      child 0, item: string
group_id: string
dataset: string
qa_count: int64
source_image: string
source_ids: list<item: string>
  child 0, item: string
timestamps_sec: list<item: int64>
  child 0, item: int64
split: string
question_types: list<item: string>
  child 0, item: string
fold: int64
label_vector: list<item: int64>
  child 0, item: int64
video: string
labels: list<item: string>
  child 0, item: string
source_numeric_ids: list<item: int64>
  child 0, item: int64
label_names: list<item: string>
  child 0, item: string
dataset_short: string
to
{'dataset': Value('string'), 'dataset_short': Value('string'), 'fold': Value('int64'), 'group_id': Value('string'), 'id': Value('string'), 'image': Value('string'), 'label_names': List(Value('string')), 'label_vector': List(Value('int64')), 'labels': List(Value('string')), 'qa_count': Value('int64'), 'question_types': List(Value('string')), 'source_ids': List(Value('string')), 'source_image': Value('string'), 'source_numeric_ids': List(Value('int64')), 'split': Value('string'), 'timestamps_sec': List(Value('int64')), 'video': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              image: string
              conversations: list<item: struct<from: string, value: string>>
                child 0, item: struct<from: string, value: string>
                    child 0, from: string
                    child 1, value: string
              meta: struct<dataset: string, dataset_short: string, split: string, track: string, source_id: int64, video (... 357 chars omitted)
                child 0, dataset: string
                child 1, dataset_short: string
                child 2, split: string
                child 3, track: string
                child 4, source_id: int64
                child 5, video: string
                child 6, timestamp_start: string
                child 7, timestamp_end: string
                child 8, timestamp_sec: int64
                child 9, frame_index: int64
                child 10, video_frame_count: int64
                child 11, video_duration_sec: double
                child 12, procedure_type: string
                child 13, answer_format: string
                child 14, answer_format_norm: string
                child 15, generation: string
                child 16, clinical_relevance: bool
                child 17, ood: bool
                child 18, primary_capability: string
                child 19, secondary_capabilities: list<item: string>
                    child 0, item: string
              group_id: string
              dataset: string
              qa_count: int64
              source_image: string
              source_ids: list<item: string>
                child 0, item: string
              timestamps_sec: list<item: int64>
                child 0, item: int64
              split: string
              question_types: list<item: string>
                child 0, item: string
              fold: int64
              label_vector: list<item: int64>
                child 0, item: int64
              video: string
              labels: list<item: string>
                child 0, item: string
              source_numeric_ids: list<item: int64>
                child 0, item: int64
              label_names: list<item: string>
                child 0, item: string
              dataset_short: string
              to
              {'dataset': Value('string'), 'dataset_short': Value('string'), 'fold': Value('int64'), 'group_id': Value('string'), 'id': Value('string'), 'image': Value('string'), 'label_names': List(Value('string')), 'label_vector': List(Value('int64')), 'labels': List(Value('string')), 'qa_count': Value('int64'), 'question_types': List(Value('string')), 'source_ids': List(Value('string')), 'source_image': Value('string'), 'source_numeric_ids': List(Value('int64')), 'split': Value('string'), 'timestamps_sec': List(Value('int64')), 'video': Value('string')}
              because column names don't match
              
              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/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset