Datasets:

---

CEIA-RL

/

energy-eval-filtered_evaluations_v3

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CEIA Reinforcement Learning 9

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model: large_string
encoder_model: large_string
reranker_model: large_string
rag: large_string
method: large_string
corpus_tag: large_string
accuracy: double
refusal: int64
request_error: int64
bad_format: int64
empty: int64
thinking: bool
-- schema metadata --
huggingface: '{"info": {"features": {"model": {"dtype": "large_string", "' + 614
to
{'model': Value('string'), 'encoder_model': Value('string'), 'reranker_model': Value('string'), 'rag': Value('string'), 'method': Value('string'), 'corpus_tag': Value('string'), 'accuracy': Value('float64'), 'refusal': Value('int64'), 'request_error': Value('int64'), 'bad_format': Value('int64'), 'empty': Value('int64')}
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 478, 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
              model: large_string
              encoder_model: large_string
              reranker_model: large_string
              rag: large_string
              method: large_string
              corpus_tag: large_string
              accuracy: double
              refusal: int64
              request_error: int64
              bad_format: int64
              empty: int64
              thinking: bool
              -- schema metadata --
              huggingface: '{"info": {"features": {"model": {"dtype": "large_string", "' + 614
              to
              {'model': Value('string'), 'encoder_model': Value('string'), 'reranker_model': Value('string'), 'rag': Value('string'), 'method': Value('string'), 'corpus_tag': Value('string'), 'accuracy': Value('float64'), 'refusal': Value('int64'), 'request_error': Value('int64'), 'bad_format': Value('int64'), 'empty': Value('int64')}
              because column names don't match