Datasets:

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saicr

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Full-Model-Background-Processing

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saicr 20

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model: string
loader: string
stack: string
n_layers: int64
hidden_size: int64
target_sequences: int64
batch_size: int64
max_input_tokens: int64
prompt_pipeline: string
sources: list<item: string>
  child 0, item: string
formats: list<item: string>
  child 0, item: string
grouping: string
reference_prompt_tokenizer: string
seed: int64
min_input_tokens: int64
top_fraction: double
strong_multiplier: double
finished_at: timestamp[s]
top_channels: list<item: struct<model: string, feature_index: int64, persistent_channel_score: double, layer_count (... 413 chars omitted)
  child 0, item: struct<model: string, feature_index: int64, persistent_channel_score: double, layer_count: int64, la (... 401 chars omitted)
      child 0, model: string
      child 1, feature_index: int64
      child 2, persistent_channel_score: double
      child 3, layer_count: int64
      child 4, layer_coverage: double
      child 5, near_universal_layer_fraction: double
      child 6, mean_universal_score: double
      child 7, min_universal_score: double
      child 8, min_worst_group_sign_consistency: double
      child 9, min_worst_group_strong_coverage: double
      child 10, mean_topk_coverage: double
      child 11, median_relative_strength: double
      child 12, median_magnitude_cv: double
      child 13, cross_layer_sign_stability: double
      child 14, mean_activation_across_layers: double
      child 15, max_abs_mean_activation: double
sequences: int64
complete: bool
to
{'model': Value('string'), 'complete': Value('bool'), 'prompt_pipeline': Value('string'), 'sequences': Value('int64'), 'n_layers': Value('int64'), 'hidden_size': Value('int64'), 'top_channels': List({'model': Value('string'), 'feature_index': Value('int64'), 'persistent_channel_score': Value('float64'), 'layer_count': Value('int64'), 'layer_coverage': Value('float64'), 'near_universal_layer_fraction': Value('float64'), 'mean_universal_score': Value('float64'), 'min_universal_score': Value('float64'), 'min_worst_group_sign_consistency': Value('float64'), 'min_worst_group_strong_coverage': Value('float64'), 'mean_topk_coverage': Value('float64'), 'median_relative_strength': Value('float64'), 'median_magnitude_cv': Value('float64'), 'cross_layer_sign_stability': Value('float64'), 'mean_activation_across_layers': Value('float64'), 'max_abs_mean_activation': Value('float64')}), 'finished_at': Value('timestamp[s]')}
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 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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
              model: string
              loader: string
              stack: string
              n_layers: int64
              hidden_size: int64
              target_sequences: int64
              batch_size: int64
              max_input_tokens: int64
              prompt_pipeline: string
              sources: list<item: string>
                child 0, item: string
              formats: list<item: string>
                child 0, item: string
              grouping: string
              reference_prompt_tokenizer: string
              seed: int64
              min_input_tokens: int64
              top_fraction: double
              strong_multiplier: double
              finished_at: timestamp[s]
              top_channels: list<item: struct<model: string, feature_index: int64, persistent_channel_score: double, layer_count (... 413 chars omitted)
                child 0, item: struct<model: string, feature_index: int64, persistent_channel_score: double, layer_count: int64, la (... 401 chars omitted)
                    child 0, model: string
                    child 1, feature_index: int64
                    child 2, persistent_channel_score: double
                    child 3, layer_count: int64
                    child 4, layer_coverage: double
                    child 5, near_universal_layer_fraction: double
                    child 6, mean_universal_score: double
                    child 7, min_universal_score: double
                    child 8, min_worst_group_sign_consistency: double
                    child 9, min_worst_group_strong_coverage: double
                    child 10, mean_topk_coverage: double
                    child 11, median_relative_strength: double
                    child 12, median_magnitude_cv: double
                    child 13, cross_layer_sign_stability: double
                    child 14, mean_activation_across_layers: double
                    child 15, max_abs_mean_activation: double
              sequences: int64
              complete: bool
              to
              {'model': Value('string'), 'complete': Value('bool'), 'prompt_pipeline': Value('string'), 'sequences': Value('int64'), 'n_layers': Value('int64'), 'hidden_size': Value('int64'), 'top_channels': List({'model': Value('string'), 'feature_index': Value('int64'), 'persistent_channel_score': Value('float64'), 'layer_count': Value('int64'), 'layer_coverage': Value('float64'), 'near_universal_layer_fraction': Value('float64'), 'mean_universal_score': Value('float64'), 'min_universal_score': Value('float64'), 'min_worst_group_sign_consistency': Value('float64'), 'min_worst_group_strong_coverage': Value('float64'), 'mean_topk_coverage': Value('float64'), 'median_relative_strength': Value('float64'), 'median_magnitude_cv': Value('float64'), 'cross_layer_sign_stability': Value('float64'), 'mean_activation_across_layers': Value('float64'), 'max_abs_mean_activation': Value('float64')}), 'finished_at': Value('timestamp[s]')}
              because column names don't match