Datasets:

---

miguelcsx

/

factorized-strict-small-priors

like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
text: string
source_repo: string
microdocuments: struct<null_assignment: string, occurrence_cap_per_anchor: int64, pair_words: int64, scheduler: stri (... 3 chars omitted)
  child 0, null_assignment: string
  child 1, occurrence_cap_per_anchor: int64
  child 2, pair_words: int64
  child 3, scheduler: string
schema_version: int64
source_revision: string
induction_words: int64
document_disjoint: bool
partitions: struct<holdout: int64, inducer: int64, lm: int64>
  child 0, holdout: int64
  child 1, inducer: int64
  child 2, lm: int64
files: struct<brown_labels.npy: string, brown_labels_null.npy: string, conceptual_exclusions.npy: string, c (... 281 chars omitted)
  child 0, brown_labels.npy: string
  child 1, brown_labels_null.npy: string
  child 2, conceptual_exclusions.npy: string
  child 3, conceptual_exclusions_null.npy: string
  child 4, conceptual_neighbors.npy: string
  child 5, conceptual_neighbors_null.npy: string
  child 6, conceptual_weights.npy: string
  child 7, conceptual_weights_null.npy: string
  child 8, match_keys.npy: string
  child 9, vocabulary.json: string
  child 10, word_hash_ids.npy: string
  child 11, word_hashes.npy: string
word_identity_hash: string
partition_proof: struct<holdout: struct<document_ids_sha256: string, documents: int64, text_sha256: string, words: in (... 198 chars omitted)
  child 0, holdout: struct<document_ids_sha256: string, documents: int64, text_sha256: string, words: int64>
      child 0, document_ids_sha256: string
      child 1
...
 child 5, tokenizer_builder_sha256: string
relational_words: struct<brown: int64, ppmi: int64>
  child 0, brown: int64
  child 1, ppmi: int64
brown: struct<clusters: int64, prefix_bits: list<item: int64>, vocabulary: int64>
  child 0, clusters: int64
  child 1, prefix_bits: list<item: int64>
      child 0, item: int64
  child 2, vocabulary: int64
ppmi: struct<context_smoothing: double, distance_weight: string, folds: int64, minimum_anchor_coverage: do (... 143 chars omitted)
  child 0, context_smoothing: double
  child 1, distance_weight: string
  child 2, folds: int64
  child 3, minimum_anchor_coverage: double
  child 4, minimum_fold_presence: int64
  child 5, minimum_pair_count: int64
  child 6, minimum_stable_edges: int64
  child 7, neighbors: int64
  child 8, vocabulary: int64
  child 9, window: int64
corpus_null_audit: struct<brown: struct<pairs: int64, retained_by_prefix: list<item: int64>, retained_fraction_by_prefi (... 100 chars omitted)
  child 0, brown: struct<pairs: int64, retained_by_prefix: list<item: int64>, retained_fraction_by_prefix: list<item:  (... 8 chars omitted)
      child 0, pairs: int64
      child 1, retained_by_prefix: list<item: int64>
          child 0, item: int64
      child 2, retained_fraction_by_prefix: list<item: double>
          child 0, item: double
  child 1, ppmi: struct<pairs: int64, retained_direct: int64, retained_two_hop: int64>
      child 0, pairs: int64
      child 1, retained_direct: int64
      child 2, retained_two_hop: int64
to
{'brown': {'clusters': Value('int64'), 'prefix_bits': List(Value('int64')), 'vocabulary': Value('int64')}, 'corpus_null_audit': {'brown': {'pairs': Value('int64'), 'retained_by_prefix': List(Value('int64')), 'retained_fraction_by_prefix': List(Value('float64'))}, 'ppmi': {'pairs': Value('int64'), 'retained_direct': Value('int64'), 'retained_two_hop': Value('int64')}}, 'document_disjoint': Value('bool'), 'files': {'brown_labels.npy': Value('string'), 'brown_labels_null.npy': Value('string'), 'conceptual_exclusions.npy': Value('string'), 'conceptual_exclusions_null.npy': Value('string'), 'conceptual_neighbors.npy': Value('string'), 'conceptual_neighbors_null.npy': Value('string'), 'conceptual_weights.npy': Value('string'), 'conceptual_weights_null.npy': Value('string'), 'match_keys.npy': Value('string'), 'vocabulary.json': Value('string'), 'word_hash_ids.npy': Value('string'), 'word_hashes.npy': Value('string')}, 'induction_words': Value('int64'), 'microdocuments': {'null_assignment': Value('string'), 'occurrence_cap_per_anchor': Value('int64'), 'pair_words': Value('int64'), 'scheduler': Value('string')}, 'partition_proof': {'holdout': {'document_ids_sha256': Value('string'), 'documents': Value('int64'), 'text_sha256': Value('string'), 'words': Value('int64')}, 'inducer': {'document_ids_sha256': Value('string'), 'documents': Value('int64'), 'text_sha256': Value('string'), 'words': Value('int64')}, 'lm': {'document_ids_sha256': Value('string'), 'documents': Value('int64'), 'text_sha256': Value('string'), 'words': Value('int64')}}, 'partitions': {'holdout': Value('int64'), 'inducer': Value('int64'), 'lm': Value('int64')}, 'ppmi': {'context_smoothing': Value('float64'), 'distance_weight': Value('string'), 'folds': Value('int64'), 'minimum_anchor_coverage': Value('float64'), 'minimum_fold_presence': Value('int64'), 'minimum_pair_count': Value('int64'), 'minimum_stable_edges': Value('int64'), 'neighbors': Value('int64'), 'vocabulary': Value('int64'), 'window': Value('int64')}, 'prior_audit': {'brown_top_5k_coverage': Value('float64'), 'matched_null': {'degree_exact': Value('bool'), 'null_edge_overlap': Value('float64'), 'null_swaps': Value('int64'), 'strength_exact': Value('bool')}, 'ppmi': {'mean_degree': Value('float64'), 'stability_threshold': Value('float64'), 'stable_edges': Value('int64')}, 'ppmi_top_2k_anchor_coverage': Value('float64')}, 'provenance': {'brown_binary_sha256': Value('string'), 'brown_commit': Value('string'), 'builder_sha256': Value('string'), 'graph_builder_sha256': Value('string'), 'source_data_sha256': Value('string'), 'tokenizer_builder_sha256': Value('string')}, 'relational_words': {'brown': Value('int64'), 'ppmi': Value('int64')}, 'schema_version': Value('int64'), 'source_repo': Value('string'), 'source_revision': Value('string'), 'word_identity_hash': Value('string')}
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/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
              text: string
              source_repo: string
              microdocuments: struct<null_assignment: string, occurrence_cap_per_anchor: int64, pair_words: int64, scheduler: stri (... 3 chars omitted)
                child 0, null_assignment: string
                child 1, occurrence_cap_per_anchor: int64
                child 2, pair_words: int64
                child 3, scheduler: string
              schema_version: int64
              source_revision: string
              induction_words: int64
              document_disjoint: bool
              partitions: struct<holdout: int64, inducer: int64, lm: int64>
                child 0, holdout: int64
                child 1, inducer: int64
                child 2, lm: int64
              files: struct<brown_labels.npy: string, brown_labels_null.npy: string, conceptual_exclusions.npy: string, c (... 281 chars omitted)
                child 0, brown_labels.npy: string
                child 1, brown_labels_null.npy: string
                child 2, conceptual_exclusions.npy: string
                child 3, conceptual_exclusions_null.npy: string
                child 4, conceptual_neighbors.npy: string
                child 5, conceptual_neighbors_null.npy: string
                child 6, conceptual_weights.npy: string
                child 7, conceptual_weights_null.npy: string
                child 8, match_keys.npy: string
                child 9, vocabulary.json: string
                child 10, word_hash_ids.npy: string
                child 11, word_hashes.npy: string
              word_identity_hash: string
              partition_proof: struct<holdout: struct<document_ids_sha256: string, documents: int64, text_sha256: string, words: in (... 198 chars omitted)
                child 0, holdout: struct<document_ids_sha256: string, documents: int64, text_sha256: string, words: int64>
                    child 0, document_ids_sha256: string
                    child 1
              ...
               child 5, tokenizer_builder_sha256: string
              relational_words: struct<brown: int64, ppmi: int64>
                child 0, brown: int64
                child 1, ppmi: int64
              brown: struct<clusters: int64, prefix_bits: list<item: int64>, vocabulary: int64>
                child 0, clusters: int64
                child 1, prefix_bits: list<item: int64>
                    child 0, item: int64
                child 2, vocabulary: int64
              ppmi: struct<context_smoothing: double, distance_weight: string, folds: int64, minimum_anchor_coverage: do (... 143 chars omitted)
                child 0, context_smoothing: double
                child 1, distance_weight: string
                child 2, folds: int64
                child 3, minimum_anchor_coverage: double
                child 4, minimum_fold_presence: int64
                child 5, minimum_pair_count: int64
                child 6, minimum_stable_edges: int64
                child 7, neighbors: int64
                child 8, vocabulary: int64
                child 9, window: int64
              corpus_null_audit: struct<brown: struct<pairs: int64, retained_by_prefix: list<item: int64>, retained_fraction_by_prefi (... 100 chars omitted)
                child 0, brown: struct<pairs: int64, retained_by_prefix: list<item: int64>, retained_fraction_by_prefix: list<item:  (... 8 chars omitted)
                    child 0, pairs: int64
                    child 1, retained_by_prefix: list<item: int64>
                        child 0, item: int64
                    child 2, retained_fraction_by_prefix: list<item: double>
                        child 0, item: double
                child 1, ppmi: struct<pairs: int64, retained_direct: int64, retained_two_hop: int64>
                    child 0, pairs: int64
                    child 1, retained_direct: int64
                    child 2, retained_two_hop: int64
              to
              {'brown': {'clusters': Value('int64'), 'prefix_bits': List(Value('int64')), 'vocabulary': Value('int64')}, 'corpus_null_audit': {'brown': {'pairs': Value('int64'), 'retained_by_prefix': List(Value('int64')), 'retained_fraction_by_prefix': List(Value('float64'))}, 'ppmi': {'pairs': Value('int64'), 'retained_direct': Value('int64'), 'retained_two_hop': Value('int64')}}, 'document_disjoint': Value('bool'), 'files': {'brown_labels.npy': Value('string'), 'brown_labels_null.npy': Value('string'), 'conceptual_exclusions.npy': Value('string'), 'conceptual_exclusions_null.npy': Value('string'), 'conceptual_neighbors.npy': Value('string'), 'conceptual_neighbors_null.npy': Value('string'), 'conceptual_weights.npy': Value('string'), 'conceptual_weights_null.npy': Value('string'), 'match_keys.npy': Value('string'), 'vocabulary.json': Value('string'), 'word_hash_ids.npy': Value('string'), 'word_hashes.npy': Value('string')}, 'induction_words': Value('int64'), 'microdocuments': {'null_assignment': Value('string'), 'occurrence_cap_per_anchor': Value('int64'), 'pair_words': Value('int64'), 'scheduler': Value('string')}, 'partition_proof': {'holdout': {'document_ids_sha256': Value('string'), 'documents': Value('int64'), 'text_sha256': Value('string'), 'words': Value('int64')}, 'inducer': {'document_ids_sha256': Value('string'), 'documents': Value('int64'), 'text_sha256': Value('string'), 'words': Value('int64')}, 'lm': {'document_ids_sha256': Value('string'), 'documents': Value('int64'), 'text_sha256': Value('string'), 'words': Value('int64')}}, 'partitions': {'holdout': Value('int64'), 'inducer': Value('int64'), 'lm': Value('int64')}, 'ppmi': {'context_smoothing': Value('float64'), 'distance_weight': Value('string'), 'folds': Value('int64'), 'minimum_anchor_coverage': Value('float64'), 'minimum_fold_presence': Value('int64'), 'minimum_pair_count': Value('int64'), 'minimum_stable_edges': Value('int64'), 'neighbors': Value('int64'), 'vocabulary': Value('int64'), 'window': Value('int64')}, 'prior_audit': {'brown_top_5k_coverage': Value('float64'), 'matched_null': {'degree_exact': Value('bool'), 'null_edge_overlap': Value('float64'), 'null_swaps': Value('int64'), 'strength_exact': Value('bool')}, 'ppmi': {'mean_degree': Value('float64'), 'stability_threshold': Value('float64'), 'stable_edges': Value('int64')}, 'ppmi_top_2k_anchor_coverage': Value('float64')}, 'provenance': {'brown_binary_sha256': Value('string'), 'brown_commit': Value('string'), 'builder_sha256': Value('string'), 'graph_builder_sha256': Value('string'), 'source_data_sha256': Value('string'), 'tokenizer_builder_sha256': Value('string')}, 'relational_words': {'brown': Value('int64'), 'ppmi': Value('int64')}, 'schema_version': Value('int64'), 'source_repo': Value('string'), 'source_revision': Value('string'), 'word_identity_hash': Value('string')}
              because column names don't match