Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
image: list<item: string>
child 0, item: 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<source: string, original_response: string, options: struct<A: string, B: string>, sample: str (... 79 chars omitted)
child 0, source: string
child 1, original_response: string
child 2, options: struct<A: string, B: string>
child 0, A: string
child 1, B: string
child 3, sample: string
child 4, original_image: string
child 5, pathology_label: string
child 6, pathology_option: string
test_skipped: int64
test_label_counts: struct<A: int64, B: int64>
child 0, A: int64
child 1, B: int64
output_dir: string
question: string
train_items: int64
train_test_sample_overlap_count: int64
test_vqa_json: string
test_image_root: string
metadata_csv: string
options: struct<A: string, B: string>
child 0, A: string
child 1, B: string
test_items: int64
train_csv: string
train_test_sample_overlap_examples: list<item: null>
child 0, item: null
train_skipped: int64
train_label_counts: struct<A: int64, B: int64>
child 0, A: int64
child 1, B: int64
to
{'question': Value('string'), 'options': {'A': Value('string'), 'B': Value('string')}, 'train_csv': Value('string'), 'metadata_csv': Value('string'), 'test_vqa_json': Value('string'), 'test_image_root': Value('string'), 'output_dir': Value('string'), 'train_items': Value('int64'), 'test_items': Value('int64'), 'train_skipped': Value('int64'), 'test_skipped': Value('int64'), 'train_label_counts': {'A': Value('int64'), 'B': Value('int64')}, 'test_label_counts': {'A': Value('int64'), 'B': Value('int64')}, 'train_test_sample_overlap_count': Value('int64'), 'train_test_sample_overlap_examples': List(Value('null'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
image: list<item: string>
child 0, item: 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<source: string, original_response: string, options: struct<A: string, B: string>, sample: str (... 79 chars omitted)
child 0, source: string
child 1, original_response: string
child 2, options: struct<A: string, B: string>
child 0, A: string
child 1, B: string
child 3, sample: string
child 4, original_image: string
child 5, pathology_label: string
child 6, pathology_option: string
test_skipped: int64
test_label_counts: struct<A: int64, B: int64>
child 0, A: int64
child 1, B: int64
output_dir: string
question: string
train_items: int64
train_test_sample_overlap_count: int64
test_vqa_json: string
test_image_root: string
metadata_csv: string
options: struct<A: string, B: string>
child 0, A: string
child 1, B: string
test_items: int64
train_csv: string
train_test_sample_overlap_examples: list<item: null>
child 0, item: null
train_skipped: int64
train_label_counts: struct<A: int64, B: int64>
child 0, A: int64
child 1, B: int64
to
{'question': Value('string'), 'options': {'A': Value('string'), 'B': Value('string')}, 'train_csv': Value('string'), 'metadata_csv': Value('string'), 'test_vqa_json': Value('string'), 'test_image_root': Value('string'), 'output_dir': Value('string'), 'train_items': Value('int64'), 'test_items': Value('int64'), 'train_skipped': Value('int64'), 'test_skipped': Value('int64'), 'train_label_counts': {'A': Value('int64'), 'B': Value('int64')}, 'test_label_counts': {'A': Value('int64'), 'B': Value('int64')}, 'train_test_sample_overlap_count': Value('int64'), 'train_test_sample_overlap_examples': List(Value('null'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
BUS-COT Training Bundle
This public dataset repository packages the training inputs and checkpoints used for the BUS-COT breast-ultrasound experiments.
Contents
data/training_csvs/: GMPO and positive-only CLIP training CSV files.data/image_assets/: positive images, GMPO negative images, and lesion masks.data/vqa/full_train_and_test_data/: BUS-COT VQA train/test JSON files.checkpoints/upstream/: selected GMPOoa25_like_012epoch-3 checkpoint.manifests/: GMPO-selection and evaluation summaries.
Selected GMPO
The only uploaded checkpoint is GMPO oa25_like_012, epoch 3, with global
four-way weights [0.5, 0.5, 1.0, 0.25].
Important Notes
- The UDIAT segmentation result was used to choose among GMPO candidates; it is not an independent blind-final-test estimate.
- Users are responsible for verifying the source data licence, consent status, institutional approval, and suitability for their intended use.
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