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
builtAt: string
appVersion: string
copIndexBase: int64
copDiagnostics: struct<rangeMin: int64, rangeMax: int64, markerComparable: int64, markerAgree1: int64, markerAgree0: (... 7 chars omitted)
child 0, rangeMin: int64
child 1, rangeMax: int64
child 2, markerComparable: int64
child 3, markerAgree1: int64
child 4, markerAgree0: int64
sourceFiles: list<item: struct<file: string, bytes: int64, sha256: string>>
child 0, item: struct<file: string, bytes: int64, sha256: string>
child 0, file: string
child 1, bytes: int64
child 2, sha256: string
counts: struct<raw: int64, accepted: int64, rejected: int64, duplicates: int64, conflictingAnswerGroups: int (... 27 chars omitted)
child 0, raw: int64
child 1, accepted: int64
child 2, rejected: int64
child 3, duplicates: int64
child 4, conflictingAnswerGroups: int64
child 5, sessionEligible: int64
answerIndexHistogram: list<item: int64>
child 0, item: int64
answerKeyHash: string
subjects: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
topicsBySubject: struct<Anaesthesia: list<item: struct<name: string, count: int64>>, Anatomy: list<item: struct<name: (... 1187 chars omitted)
child 0, Anaesthesia: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 1, Anatomy: list<ite
...
ount: int64
child 15, Psychiatry: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 16, Radiology: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 17, Skin: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 18, Social & Preventive Medicine: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 19, Surgery: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 20, Unknown: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
total: int64
flags: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
splits: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
to
{'subjects': List({'name': Value('string'), 'count': Value('int64')}), 'topicsBySubject': {'Anaesthesia': List({'name': Value('string'), 'count': Value('int64')}), 'Anatomy': List({'name': Value('string'), 'count': Value('int64')}), 'Biochemistry': List({'name': Value('string'), 'count': Value('int64')}), 'Dental': List({'name': Value('string'), 'count': Value('int64')}), 'ENT': List({'name': Value('string'), 'count': Value('int64')}), 'Forensic Medicine': List({'name': Value('string'), 'count': Value('int64')}), 'Gynaecology & Obstetrics': List({'name': Value('string'), 'count': Value('int64')}), 'Medicine': List({'name': Value('string'), 'count': Value('int64')}), 'Microbiology': List({'name': Value('string'), 'count': Value('int64')}), 'Ophthalmology': List({'name': Value('string'), 'count': Value('int64')}), 'Orthopaedics': List({'name': Value('string'), 'count': Value('int64')}), 'Pathology': List({'name': Value('string'), 'count': Value('int64')}), 'Pediatrics': List({'name': Value('string'), 'count': Value('int64')}), 'Pharmacology': List({'name': Value('string'), 'count': Value('int64')}), 'Physiology': List({'name': Value('string'), 'count': Value('int64')}), 'Psychiatry': List({'name': Value('string'), 'count': Value('int64')}), 'Radiology': List({'name': Value('string'), 'count': Value('int64')}), 'Skin': List({'name': Value('string'), 'count': Value('int64')}), 'Social & Preventive Medicine': List({'name': Value('string'), 'count': Value('int64')}), 'Surgery': List({'name': Value('string'), 'count': Value('int64')}), 'Unknown': List({'name': Value('string'), 'count': Value('int64')})}, 'flags': List({'name': Value('string'), 'count': Value('int64')}), 'splits': List({'name': Value('string'), 'count': Value('int64')}), 'total': Value('int64')}
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
builtAt: string
appVersion: string
copIndexBase: int64
copDiagnostics: struct<rangeMin: int64, rangeMax: int64, markerComparable: int64, markerAgree1: int64, markerAgree0: (... 7 chars omitted)
child 0, rangeMin: int64
child 1, rangeMax: int64
child 2, markerComparable: int64
child 3, markerAgree1: int64
child 4, markerAgree0: int64
sourceFiles: list<item: struct<file: string, bytes: int64, sha256: string>>
child 0, item: struct<file: string, bytes: int64, sha256: string>
child 0, file: string
child 1, bytes: int64
child 2, sha256: string
counts: struct<raw: int64, accepted: int64, rejected: int64, duplicates: int64, conflictingAnswerGroups: int (... 27 chars omitted)
child 0, raw: int64
child 1, accepted: int64
child 2, rejected: int64
child 3, duplicates: int64
child 4, conflictingAnswerGroups: int64
child 5, sessionEligible: int64
answerIndexHistogram: list<item: int64>
child 0, item: int64
answerKeyHash: string
subjects: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
topicsBySubject: struct<Anaesthesia: list<item: struct<name: string, count: int64>>, Anatomy: list<item: struct<name: (... 1187 chars omitted)
child 0, Anaesthesia: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 1, Anatomy: list<ite
...
ount: int64
child 15, Psychiatry: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 16, Radiology: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 17, Skin: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 18, Social & Preventive Medicine: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 19, Surgery: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
child 20, Unknown: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
total: int64
flags: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
splits: list<item: struct<name: string, count: int64>>
child 0, item: struct<name: string, count: int64>
child 0, name: string
child 1, count: int64
to
{'subjects': List({'name': Value('string'), 'count': Value('int64')}), 'topicsBySubject': {'Anaesthesia': List({'name': Value('string'), 'count': Value('int64')}), 'Anatomy': List({'name': Value('string'), 'count': Value('int64')}), 'Biochemistry': List({'name': Value('string'), 'count': Value('int64')}), 'Dental': List({'name': Value('string'), 'count': Value('int64')}), 'ENT': List({'name': Value('string'), 'count': Value('int64')}), 'Forensic Medicine': List({'name': Value('string'), 'count': Value('int64')}), 'Gynaecology & Obstetrics': List({'name': Value('string'), 'count': Value('int64')}), 'Medicine': List({'name': Value('string'), 'count': Value('int64')}), 'Microbiology': List({'name': Value('string'), 'count': Value('int64')}), 'Ophthalmology': List({'name': Value('string'), 'count': Value('int64')}), 'Orthopaedics': List({'name': Value('string'), 'count': Value('int64')}), 'Pathology': List({'name': Value('string'), 'count': Value('int64')}), 'Pediatrics': List({'name': Value('string'), 'count': Value('int64')}), 'Pharmacology': List({'name': Value('string'), 'count': Value('int64')}), 'Physiology': List({'name': Value('string'), 'count': Value('int64')}), 'Psychiatry': List({'name': Value('string'), 'count': Value('int64')}), 'Radiology': List({'name': Value('string'), 'count': Value('int64')}), 'Skin': List({'name': Value('string'), 'count': Value('int64')}), 'Social & Preventive Medicine': List({'name': Value('string'), 'count': Value('int64')}), 'Surgery': List({'name': Value('string'), 'count': Value('int64')}), 'Unknown': List({'name': Value('string'), 'count': Value('int64')})}, 'flags': List({'name': Value('string'), 'count': Value('int64')}), 'splits': List({'name': Value('string'), 'count': Value('int64')}), 'total': Value('int64')}
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.
MedMCQA practice corpus (build artefact)
Build output of the ETL in medmcqa-practice. Not the original dataset - this is a normalised, validated SQLite build of it, published here only so a Git-based host can download it at deploy time (it is too large for a Git repository).
| File | Purpose |
|---|---|
corpus.sqlite |
186,791 questions + FTS5 index |
manifest.json |
Build metadata, counts, and the answer-key checksum |
facets.json |
Subject/topic counts |
The application verifies manifest.json's answerKeyHash against the
database at startup and refuses to serve questions if they disagree, so a
corrupted or substituted download fails closed rather than showing wrong answers.
Source data: MedMCQA (Pal, Umapathi & Sankarasubbu, PMLR v174, 2022) - https://github.com/medmcqa/medmcqa, Apache-2.0.
Not clinical guidance. Exam-preparation material containing known errata.
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