Datasets:

---

ardauzunoglu

/

NOKI

like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
row_idx: int64
qa_index: int64
qa_id: string
question: string
candidate_answer: string
model: string
verification: struct<confidence: string, rationale: string, sensitive_domain: bool, sources: list<element: struct< (... 145 chars omitted)
  child 0, confidence: string
  child 1, rationale: string
  child 2, sensitive_domain: bool
  child 3, sources: list<element: struct<evidence: string, publisher: string, relation: string, source_class: string, ti (... 26 chars omitted)
      child 0, element: struct<evidence: string, publisher: string, relation: string, source_class: string, title: string, u (... 11 chars omitted)
          child 0, evidence: string
          child 1, publisher: string
          child 2, relation: string
          child 3, source_class: string
          child 4, title: string
          child 5, url: string
  child 4, time_sensitive: bool
  child 5, verdict: string
evidence_validation: struct<authoritative_source_count: int64, errors: list<element: string>, external_evidence_found: bo (... 82 chars omitted)
  child 0, authoritative_source_count: int64
  child 1, errors: list<element: string>
      child 0, element: string
  child 2, external_evidence_found: bool
  child 3, independent_domain_count: int64
  child 4, passed: bool
  child 5, qualifying_source_count: int64
consulted_sources: list<element: struct<type: string, url: string>>
  child 0, element: struct<type: string, url: string>
      child 0, type: string
      child 1, url: string
distractors: list<element: string>
  child 0, element: string
choices: list<element: string>
  child 0, element: string
gold_index: int64
-- schema metadata --
huggingface: '{"info": {"features": {"row_idx": {"dtype": "int64", "_type' + 1677
to
{'row_idx': Value('int64'), 'qa_index': Value('int64'), 'qa_id': Value('string'), 'question': Value('string'), 'candidate_answer': Value('string'), 'model': Value('string'), 'verification': {'confidence': Value('string'), 'rationale': Value('string'), 'sensitive_domain': Value('bool'), 'sources': List({'evidence': Value('string'), 'publisher': Value('string'), 'relation': Value('string'), 'source_class': Value('string'), 'title': Value('string'), 'url': Value('string')}), 'time_sensitive': Value('bool'), 'verdict': Value('string')}, 'evidence_validation': {'authoritative_source_count': Value('int64'), 'errors': List(Value('string')), 'external_evidence_found': Value('bool'), 'independent_domain_count': Value('int64'), 'passed': Value('bool'), 'qualifying_source_count': Value('int64')}, 'consulted_sources': List({'type': Value('string'), 'url': Value('string')}), 'distractors': List(Value('string'))}
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/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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
              row_idx: int64
              qa_index: int64
              qa_id: string
              question: string
              candidate_answer: string
              model: string
              verification: struct<confidence: string, rationale: string, sensitive_domain: bool, sources: list<element: struct< (... 145 chars omitted)
                child 0, confidence: string
                child 1, rationale: string
                child 2, sensitive_domain: bool
                child 3, sources: list<element: struct<evidence: string, publisher: string, relation: string, source_class: string, ti (... 26 chars omitted)
                    child 0, element: struct<evidence: string, publisher: string, relation: string, source_class: string, title: string, u (... 11 chars omitted)
                        child 0, evidence: string
                        child 1, publisher: string
                        child 2, relation: string
                        child 3, source_class: string
                        child 4, title: string
                        child 5, url: string
                child 4, time_sensitive: bool
                child 5, verdict: string
              evidence_validation: struct<authoritative_source_count: int64, errors: list<element: string>, external_evidence_found: bo (... 82 chars omitted)
                child 0, authoritative_source_count: int64
                child 1, errors: list<element: string>
                    child 0, element: string
                child 2, external_evidence_found: bool
                child 3, independent_domain_count: int64
                child 4, passed: bool
                child 5, qualifying_source_count: int64
              consulted_sources: list<element: struct<type: string, url: string>>
                child 0, element: struct<type: string, url: string>
                    child 0, type: string
                    child 1, url: string
              distractors: list<element: string>
                child 0, element: string
              choices: list<element: string>
                child 0, element: string
              gold_index: int64
              -- schema metadata --
              huggingface: '{"info": {"features": {"row_idx": {"dtype": "int64", "_type' + 1677
              to
              {'row_idx': Value('int64'), 'qa_index': Value('int64'), 'qa_id': Value('string'), 'question': Value('string'), 'candidate_answer': Value('string'), 'model': Value('string'), 'verification': {'confidence': Value('string'), 'rationale': Value('string'), 'sensitive_domain': Value('bool'), 'sources': List({'evidence': Value('string'), 'publisher': Value('string'), 'relation': Value('string'), 'source_class': Value('string'), 'title': Value('string'), 'url': Value('string')}), 'time_sensitive': Value('bool'), 'verdict': Value('string')}, 'evidence_validation': {'authoritative_source_count': Value('int64'), 'errors': List(Value('string')), 'external_evidence_found': Value('bool'), 'independent_domain_count': Value('int64'), 'passed': Value('bool'), 'qualifying_source_count': Value('int64')}, 'consulted_sources': List({'type': Value('string'), 'url': Value('string')}), 'distractors': List(Value('string'))}
              because column names don't match