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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
seed: int64
mode: string
note: string
exclude: struct<synth-run: list<item: null>, automathtext-v2: list<item: null>, finewebedu: list<item: null>, (... 287 chars omitted)
  child 0, synth-run: list<item: null>
      child 0, item: null
  child 1, automathtext-v2: list<item: null>
      child 0, item: null
  child 2, finewebedu: list<item: null>
      child 0, item: null
  child 3, ultradata-math-l3: list<item: null>
      child 0, item: null
  child 4, openthoughts3-1.2m: list<item: null>
      child 0, item: null
  child 5, pes2o-v3: list<item: null>
      child 0, item: null
  child 6, nemotron-cc-math-v1-4plus-mind: list<item: null>
      child 0, item: null
  child 7, nemotron-specialized-v1.2: list<item: null>
      child 0, item: null
  child 8, openmathreasoning: list<item: null>
      child 0, item: null
  child 9, cosmopedia-wikihow-stories-arrow: list<item: null>
      child 0, item: null
seq_len: int64
version: int64
total: int64
disjoint_from_train_shards: bool
actual_total: int64
source: string
datasets: list<item: struct<name: string, eval_ratio: double, cache_path: string, n_sequences: int64, eval_sha (... 484 chars omitted)
  child 0, item: struct<name: string, eval_ratio: double, cache_path: string, n_sequences: int64, eval_shard_keys: li (... 472 chars omitted)
      child 0, name: string
      child 1, eval_ratio: double
      child 2, cache_path: string
      child 3, n_sequences: int64
      child 4, eval_shard_keys: list<item: string>
          child 0, item: string
      child 5, shards: list<item: struct<key: string, npy: string, n_sequences: int64, seq_ranges: list<item: struct<seq_st (... 124 chars omitted)
          child 0, item: struct<key: string, npy: string, n_sequences: int64, seq_ranges: list<item: struct<seq_start: int64, (... 112 chars omitted)
              child 0, key: string
              child 1, npy: string
              child 2, n_sequences: int64
              child 3, seq_ranges: list<item: struct<seq_start: int64, seq_end: int64, n_sequences: int64>>
                  child 0, item: struct<seq_start: int64, seq_end: int64, n_sequences: int64>
                      child 0, seq_start: int64
                      child 1, seq_end: int64
                      child 2, n_sequences: int64
              child 4, selection: string
              child 5, n_tokens_in_shard: int64
              child 6, n_sequences_in_shard: int64
      child 6, shard_groups: null
      child 7, group_reports: null
      child 8, eval_holdout: null
      child 9, sample: struct<bucket: string, corpus_manifest: string, seed: int64, selection: string, n_sequences_requeste (... 50 chars omitted)
          child 0, bucket: string
          child 1, corpus_manifest: string
          child 2, seed: int64
          child 3, selection: string
          child 4, n_sequences_requested: int64
          child 5, n_sequences_kept: int64
          child 6, shuffled: bool
fetch_seed: int64
to
{'version': Value('int64'), 'seq_len': Value('int64'), 'total': Value('int64'), 'actual_total': Value('int64'), 'seed': Value('int64'), 'fetch_seed': Value('int64'), 'source': Value('string'), 'disjoint_from_train_shards': Value('bool'), 'note': Value('string'), 'datasets': List({'name': Value('string'), 'eval_ratio': Value('float64'), 'cache_path': Value('string'), 'n_sequences': Value('int64'), 'eval_shard_keys': List(Value('string')), 'shards': List({'key': Value('string'), 'npy': Value('string'), 'n_sequences': Value('int64'), 'seq_ranges': List({'seq_start': Value('int64'), 'seq_end': Value('int64'), 'n_sequences': Value('int64')}), 'selection': Value('string'), 'n_tokens_in_shard': Value('int64'), 'n_sequences_in_shard': Value('int64')}), 'shard_groups': Value('null'), 'group_reports': Value('null'), 'eval_holdout': Value('null'), 'sample': {'bucket': Value('string'), 'corpus_manifest': Value('string'), 'seed': Value('int64'), 'selection': Value('string'), 'n_sequences_requested': Value('int64'), 'n_sequences_kept': Value('int64'), 'shuffled': Value('bool')}})}
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
              seed: int64
              mode: string
              note: string
              exclude: struct<synth-run: list<item: null>, automathtext-v2: list<item: null>, finewebedu: list<item: null>, (... 287 chars omitted)
                child 0, synth-run: list<item: null>
                    child 0, item: null
                child 1, automathtext-v2: list<item: null>
                    child 0, item: null
                child 2, finewebedu: list<item: null>
                    child 0, item: null
                child 3, ultradata-math-l3: list<item: null>
                    child 0, item: null
                child 4, openthoughts3-1.2m: list<item: null>
                    child 0, item: null
                child 5, pes2o-v3: list<item: null>
                    child 0, item: null
                child 6, nemotron-cc-math-v1-4plus-mind: list<item: null>
                    child 0, item: null
                child 7, nemotron-specialized-v1.2: list<item: null>
                    child 0, item: null
                child 8, openmathreasoning: list<item: null>
                    child 0, item: null
                child 9, cosmopedia-wikihow-stories-arrow: list<item: null>
                    child 0, item: null
              seq_len: int64
              version: int64
              total: int64
              disjoint_from_train_shards: bool
              actual_total: int64
              source: string
              datasets: list<item: struct<name: string, eval_ratio: double, cache_path: string, n_sequences: int64, eval_sha (... 484 chars omitted)
                child 0, item: struct<name: string, eval_ratio: double, cache_path: string, n_sequences: int64, eval_shard_keys: li (... 472 chars omitted)
                    child 0, name: string
                    child 1, eval_ratio: double
                    child 2, cache_path: string
                    child 3, n_sequences: int64
                    child 4, eval_shard_keys: list<item: string>
                        child 0, item: string
                    child 5, shards: list<item: struct<key: string, npy: string, n_sequences: int64, seq_ranges: list<item: struct<seq_st (... 124 chars omitted)
                        child 0, item: struct<key: string, npy: string, n_sequences: int64, seq_ranges: list<item: struct<seq_start: int64, (... 112 chars omitted)
                            child 0, key: string
                            child 1, npy: string
                            child 2, n_sequences: int64
                            child 3, seq_ranges: list<item: struct<seq_start: int64, seq_end: int64, n_sequences: int64>>
                                child 0, item: struct<seq_start: int64, seq_end: int64, n_sequences: int64>
                                    child 0, seq_start: int64
                                    child 1, seq_end: int64
                                    child 2, n_sequences: int64
                            child 4, selection: string
                            child 5, n_tokens_in_shard: int64
                            child 6, n_sequences_in_shard: int64
                    child 6, shard_groups: null
                    child 7, group_reports: null
                    child 8, eval_holdout: null
                    child 9, sample: struct<bucket: string, corpus_manifest: string, seed: int64, selection: string, n_sequences_requeste (... 50 chars omitted)
                        child 0, bucket: string
                        child 1, corpus_manifest: string
                        child 2, seed: int64
                        child 3, selection: string
                        child 4, n_sequences_requested: int64
                        child 5, n_sequences_kept: int64
                        child 6, shuffled: bool
              fetch_seed: int64
              to
              {'version': Value('int64'), 'seq_len': Value('int64'), 'total': Value('int64'), 'actual_total': Value('int64'), 'seed': Value('int64'), 'fetch_seed': Value('int64'), 'source': Value('string'), 'disjoint_from_train_shards': Value('bool'), 'note': Value('string'), 'datasets': List({'name': Value('string'), 'eval_ratio': Value('float64'), 'cache_path': Value('string'), 'n_sequences': Value('int64'), 'eval_shard_keys': List(Value('string')), 'shards': List({'key': Value('string'), 'npy': Value('string'), 'n_sequences': Value('int64'), 'seq_ranges': List({'seq_start': Value('int64'), 'seq_end': Value('int64'), 'n_sequences': Value('int64')}), 'selection': Value('string'), 'n_tokens_in_shard': Value('int64'), 'n_sequences_in_shard': Value('int64')}), 'shard_groups': Value('null'), 'group_reports': Value('null'), 'eval_holdout': Value('null'), 'sample': {'bucket': Value('string'), 'corpus_manifest': Value('string'), 'seed': Value('int64'), 'selection': Value('string'), 'n_sequences_requested': Value('int64'), 'n_sequences_kept': Value('int64'), 'shuffled': Value('bool')}})}
              because column names don't match

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