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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
results: struct<piqa: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,non (... 8009 chars omitted)
  child 0, piqa: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double, ac (... 50 chars omitted)
      child 0, name: string
      child 1, alias: string
      child 2, sample_len: int64
      child 3, acc,none: double
      child 4, acc_stderr,none: double
      child 5, acc_norm,none: double
      child 6, acc_norm_stderr,none: double
  child 1, hellaswag: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double, ac (... 50 chars omitted)
      child 0, name: string
      child 1, alias: string
      child 2, sample_len: int64
      child 3, acc,none: double
      child 4, acc_stderr,none: double
      child 5, acc_norm,none: double
      child 6, acc_norm_stderr,none: double
  child 2, mmlu_abstract_algebra: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double>
      child 0, name: string
      child 1, alias: string
      child 2, sample_len: int64
      child 3, acc,none: double
      child 4, acc_stderr,none: double
  child 3, mmlu_anatomy: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double>
      child 0, name: string
      child 1, alias: string
      child 2, sample_len: int64
      child 3, acc,none: double
      child 4, acc_stderr,none: double
  child 4, mmlu_a
...
_virology: string
  child 34, mmlu_econometrics: string
  child 35, mmlu_high_school_geography: string
  child 36, mmlu_high_school_government_and_politics: string
  child 37, mmlu_high_school_macroeconomics: string
  child 38, mmlu_high_school_microeconomics: string
  child 39, mmlu_high_school_psychology: string
  child 40, mmlu_human_sexuality: string
  child 41, mmlu_professional_psychology: string
  child 42, mmlu_public_relations: string
  child 43, mmlu_security_studies: string
  child 44, mmlu_sociology: string
  child 45, mmlu_us_foreign_policy: string
  child 46, mmlu_formal_logic: string
  child 47, mmlu_high_school_european_history: string
  child 48, mmlu_high_school_us_history: string
  child 49, mmlu_high_school_world_history: string
  child 50, mmlu_international_law: string
  child 51, mmlu_jurisprudence: string
  child 52, mmlu_logical_fallacies: string
  child 53, mmlu_moral_disputes: string
  child 54, mmlu_moral_scenarios: string
  child 55, mmlu_philosophy: string
  child 56, mmlu_prehistory: string
  child 57, mmlu_professional_law: string
  child 58, mmlu_world_religions: string
model_source: string
model_name: string
model_name_sanitized: string
system_instruction: null
system_instruction_sha: null
fewshot_as_multiturn: null
chat_template: null
chat_template_sha: null
total_evaluation_time_seconds: string
mean_score: double
model: string
backend: string
commands: list<item: string>
  child 0, item: string
healed: list<item: null>
  child 0, item: null
to
{'model': Value('string'), 'backend': Value('string'), 'mean_score': Value('float64'), 'results': {'piqa': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}, 'hellaswag': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}, 'mmlu': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}, 'gsm8k': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}}, 'commands': List(Value('string')), 'healed': List(Value('null'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 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
              results: struct<piqa: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,non (... 8009 chars omitted)
                child 0, piqa: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double, ac (... 50 chars omitted)
                    child 0, name: string
                    child 1, alias: string
                    child 2, sample_len: int64
                    child 3, acc,none: double
                    child 4, acc_stderr,none: double
                    child 5, acc_norm,none: double
                    child 6, acc_norm_stderr,none: double
                child 1, hellaswag: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double, ac (... 50 chars omitted)
                    child 0, name: string
                    child 1, alias: string
                    child 2, sample_len: int64
                    child 3, acc,none: double
                    child 4, acc_stderr,none: double
                    child 5, acc_norm,none: double
                    child 6, acc_norm_stderr,none: double
                child 2, mmlu_abstract_algebra: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double>
                    child 0, name: string
                    child 1, alias: string
                    child 2, sample_len: int64
                    child 3, acc,none: double
                    child 4, acc_stderr,none: double
                child 3, mmlu_anatomy: struct<name: string, alias: string, sample_len: int64, acc,none: double, acc_stderr,none: double>
                    child 0, name: string
                    child 1, alias: string
                    child 2, sample_len: int64
                    child 3, acc,none: double
                    child 4, acc_stderr,none: double
                child 4, mmlu_a
              ...
              _virology: string
                child 34, mmlu_econometrics: string
                child 35, mmlu_high_school_geography: string
                child 36, mmlu_high_school_government_and_politics: string
                child 37, mmlu_high_school_macroeconomics: string
                child 38, mmlu_high_school_microeconomics: string
                child 39, mmlu_high_school_psychology: string
                child 40, mmlu_human_sexuality: string
                child 41, mmlu_professional_psychology: string
                child 42, mmlu_public_relations: string
                child 43, mmlu_security_studies: string
                child 44, mmlu_sociology: string
                child 45, mmlu_us_foreign_policy: string
                child 46, mmlu_formal_logic: string
                child 47, mmlu_high_school_european_history: string
                child 48, mmlu_high_school_us_history: string
                child 49, mmlu_high_school_world_history: string
                child 50, mmlu_international_law: string
                child 51, mmlu_jurisprudence: string
                child 52, mmlu_logical_fallacies: string
                child 53, mmlu_moral_disputes: string
                child 54, mmlu_moral_scenarios: string
                child 55, mmlu_philosophy: string
                child 56, mmlu_prehistory: string
                child 57, mmlu_professional_law: string
                child 58, mmlu_world_religions: string
              model_source: string
              model_name: string
              model_name_sanitized: string
              system_instruction: null
              system_instruction_sha: null
              fewshot_as_multiturn: null
              chat_template: null
              chat_template_sha: null
              total_evaluation_time_seconds: string
              mean_score: double
              model: string
              backend: string
              commands: list<item: string>
                child 0, item: string
              healed: list<item: null>
                child 0, item: null
              to
              {'model': Value('string'), 'backend': Value('string'), 'mean_score': Value('float64'), 'results': {'piqa': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}, 'hellaswag': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}, 'mmlu': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}, 'gsm8k': {'score': Value('float64'), 'metric': Value('string'), 'num_samples': Value('int64')}}, 'commands': List(Value('string')), 'healed': List(Value('null'))}
              because column names don't match

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