Datasets:

---

Godel-Labs-Ai

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FP-FN-Collection

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Gödel-Labs 9

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
adjudication: struct<families: list<item: string>, method: string, rationales: list<item: string>, review_basis: s (... 6 chars omitted)
  child 0, families: list<item: string>
      child 0, item: string
  child 1, method: string
  child 2, rationales: list<item: string>
      child 0, item: string
  child 3, review_basis: string
context: struct<direction: string, surface: string, tool: string>
  child 0, direction: string
  child 1, surface: string
  child 2, tool: string
detector: string
distribution: struct<contains_internal_identity_metadata: bool, intended_use: string, license: string, redistribut (... 32 chars omitted)
  child 0, contains_internal_identity_metadata: bool
  child 1, intended_use: string
  child 2, license: string
  child 3, redistributable: bool
  child 4, source_is_oss: bool
expected_disposition: string
ground_truth_label: string
ground_truth_labels: list<item: string>
  child 0, item: string
historical_detection: struct<dispositions: list<item: string>, intent_states: list<item: string>, top_margin_heads: list<i (... 49 chars omitted)
  child 0, dispositions: list<item: string>
      child 0, item: string
  child 1, intent_states: list<item: string>
      child 0, item: string
  child 2, top_margin_heads: list<item: struct<label: string, probability: double>>
      child 0, item: struct<label: string, probability: double>
          child 0, label: string
          child 1, probability: double
judgment: string
provenance: list<item: struct<path: string
...
nts: int64
  child 3, harmful_out_of_scope_events_excluded: int64
  child 4, dataset_candidate_events: int64
qualified_at: timestamp[s]
input_identity: struct<adjudication_csv_rows: int64, adjudication_csv_sha256: string, detections_report_sha256: stri (... 66 chars omitted)
  child 0, adjudication_csv_rows: int64
  child 1, adjudication_csv_sha256: string
  child 2, detections_report_sha256: string
  child 3, source_overrides_rows: int64
  child 4, source_overrides_sha256: string
publication_guards: struct<scanner_output_used_as_ground_truth: bool, source_exact_text_required: bool, truncated_captur (... 165 chars omitted)
  child 0, scanner_output_used_as_ground_truth: bool
  child 1, source_exact_text_required: bool
  child 2, truncated_captures_allowed: bool
  child 3, redacted_captures_allowed: bool
  child 4, hash_mismatches_allowed: bool
  child 5, internal_event_ids_in_dataset: bool
  child 6, harmful_out_of_scope_samples_in_dataset: bool
dataset: struct<event_rows_before_deduplication: int64, canonical_records: int64, surfaces: struct<doc_chunk: (... 132 chars omitted)
  child 0, event_rows_before_deduplication: int64
  child 1, canonical_records: int64
  child 2, surfaces: struct<doc_chunk: int64>
      child 0, doc_chunk: int64
  child 3, adjudication_families: struct<fp_benign_code_or_data: int64, fp_legitimate_agent_instruction: int64>
      child 0, fp_benign_code_or_data: int64
      child 1, fp_legitimate_agent_instruction: int64
  child 4, jsonl_sha256: string
to
{'schema_version': Value('string'), 'qualified_at': Value('timestamp[s]'), 'scope': {'detector': Value('string'), 'routing_profile': Value('string'), 'normalization': Value('bool'), 'document_intelligence': Value('bool'), 'contract_labels': List(Value('string'))}, 'cohort': {'original_classifier_review_events': Value('int64'), 'independently_adjudicated_false_positive_events': Value('int64'), 'independently_adjudicated_security_positive_events': Value('int64'), 'harmful_out_of_scope_events_excluded': Value('int64'), 'dataset_candidate_events': Value('int64')}, 'current_hp_replay': {'all_745_adjudicated_false_positives': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}, '719_after_harmful_bucket_exclusion': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}, '26_harmful_out_of_scope_excluded': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}}, 'source_quality_gate': {'source_exact_candidate_events': Value('int64'), 'inexact_report_capture_events_excluded': Value('int64'), 'source_exact_dispositions': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}, 'full_source_overrides_from_github': Value('int64'), 'full_source_override_outcomes': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}}, 'dataset': {'event_rows_before_deduplication': Value('int64'), 'canonical_records': Value('int64'), 'surfaces': {'doc_chunk': Value('int64')}, 'adjudication_families': {'fp_benign_code_or_data': Value('int64'), 'fp_legitimate_agent_instruction': Value('int64')}, 'jsonl_sha256': Value('string')}, 'qualification_identity': {'ggate_tooling_commit': Value('string'), 'scanner_source_commit': Value('string'), 'scanner_binary_sha256': Value('string'), 'scanner_contract': Value('string'), 'model_bundle_sha256': Value('string'), 'router_contract_sha256': Value('string'), 'dlp_catalog_sha256': Value('string')}, 'input_identity': {'adjudication_csv_rows': Value('int64'), 'adjudication_csv_sha256': Value('string'), 'detections_report_sha256': Value('string'), 'source_overrides_rows': Value('int64'), 'source_overrides_sha256': Value('string')}, 'publication_guards': {'scanner_output_used_as_ground_truth': Value('bool'), 'source_exact_text_required': Value('bool'), 'truncated_captures_allowed': Value('bool'), 'redacted_captures_allowed': Value('bool'), 'hash_mismatches_allowed': Value('bool'), 'internal_event_ids_in_dataset': Value('bool'), 'harmful_out_of_scope_samples_in_dataset': Value('bool')}}
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
              adjudication: struct<families: list<item: string>, method: string, rationales: list<item: string>, review_basis: s (... 6 chars omitted)
                child 0, families: list<item: string>
                    child 0, item: string
                child 1, method: string
                child 2, rationales: list<item: string>
                    child 0, item: string
                child 3, review_basis: string
              context: struct<direction: string, surface: string, tool: string>
                child 0, direction: string
                child 1, surface: string
                child 2, tool: string
              detector: string
              distribution: struct<contains_internal_identity_metadata: bool, intended_use: string, license: string, redistribut (... 32 chars omitted)
                child 0, contains_internal_identity_metadata: bool
                child 1, intended_use: string
                child 2, license: string
                child 3, redistributable: bool
                child 4, source_is_oss: bool
              expected_disposition: string
              ground_truth_label: string
              ground_truth_labels: list<item: string>
                child 0, item: string
              historical_detection: struct<dispositions: list<item: string>, intent_states: list<item: string>, top_margin_heads: list<i (... 49 chars omitted)
                child 0, dispositions: list<item: string>
                    child 0, item: string
                child 1, intent_states: list<item: string>
                    child 0, item: string
                child 2, top_margin_heads: list<item: struct<label: string, probability: double>>
                    child 0, item: struct<label: string, probability: double>
                        child 0, label: string
                        child 1, probability: double
              judgment: string
              provenance: list<item: struct<path: string
              ...
              nts: int64
                child 3, harmful_out_of_scope_events_excluded: int64
                child 4, dataset_candidate_events: int64
              qualified_at: timestamp[s]
              input_identity: struct<adjudication_csv_rows: int64, adjudication_csv_sha256: string, detections_report_sha256: stri (... 66 chars omitted)
                child 0, adjudication_csv_rows: int64
                child 1, adjudication_csv_sha256: string
                child 2, detections_report_sha256: string
                child 3, source_overrides_rows: int64
                child 4, source_overrides_sha256: string
              publication_guards: struct<scanner_output_used_as_ground_truth: bool, source_exact_text_required: bool, truncated_captur (... 165 chars omitted)
                child 0, scanner_output_used_as_ground_truth: bool
                child 1, source_exact_text_required: bool
                child 2, truncated_captures_allowed: bool
                child 3, redacted_captures_allowed: bool
                child 4, hash_mismatches_allowed: bool
                child 5, internal_event_ids_in_dataset: bool
                child 6, harmful_out_of_scope_samples_in_dataset: bool
              dataset: struct<event_rows_before_deduplication: int64, canonical_records: int64, surfaces: struct<doc_chunk: (... 132 chars omitted)
                child 0, event_rows_before_deduplication: int64
                child 1, canonical_records: int64
                child 2, surfaces: struct<doc_chunk: int64>
                    child 0, doc_chunk: int64
                child 3, adjudication_families: struct<fp_benign_code_or_data: int64, fp_legitimate_agent_instruction: int64>
                    child 0, fp_benign_code_or_data: int64
                    child 1, fp_legitimate_agent_instruction: int64
                child 4, jsonl_sha256: string
              to
              {'schema_version': Value('string'), 'qualified_at': Value('timestamp[s]'), 'scope': {'detector': Value('string'), 'routing_profile': Value('string'), 'normalization': Value('bool'), 'document_intelligence': Value('bool'), 'contract_labels': List(Value('string'))}, 'cohort': {'original_classifier_review_events': Value('int64'), 'independently_adjudicated_false_positive_events': Value('int64'), 'independently_adjudicated_security_positive_events': Value('int64'), 'harmful_out_of_scope_events_excluded': Value('int64'), 'dataset_candidate_events': Value('int64')}, 'current_hp_replay': {'all_745_adjudicated_false_positives': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}, '719_after_harmful_bucket_exclusion': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}, '26_harmful_out_of_scope_excluded': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}}, 'source_quality_gate': {'source_exact_candidate_events': Value('int64'), 'inexact_report_capture_events_excluded': Value('int64'), 'source_exact_dispositions': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}, 'full_source_overrides_from_github': Value('int64'), 'full_source_override_outcomes': {'allow': Value('int64'), 'review': Value('int64'), 'malicious': Value('int64')}}, 'dataset': {'event_rows_before_deduplication': Value('int64'), 'canonical_records': Value('int64'), 'surfaces': {'doc_chunk': Value('int64')}, 'adjudication_families': {'fp_benign_code_or_data': Value('int64'), 'fp_legitimate_agent_instruction': Value('int64')}, 'jsonl_sha256': Value('string')}, 'qualification_identity': {'ggate_tooling_commit': Value('string'), 'scanner_source_commit': Value('string'), 'scanner_binary_sha256': Value('string'), 'scanner_contract': Value('string'), 'model_bundle_sha256': Value('string'), 'router_contract_sha256': Value('string'), 'dlp_catalog_sha256': Value('string')}, 'input_identity': {'adjudication_csv_rows': Value('int64'), 'adjudication_csv_sha256': Value('string'), 'detections_report_sha256': Value('string'), 'source_overrides_rows': Value('int64'), 'source_overrides_sha256': Value('string')}, 'publication_guards': {'scanner_output_used_as_ground_truth': Value('bool'), 'source_exact_text_required': Value('bool'), 'truncated_captures_allowed': Value('bool'), 'redacted_captures_allowed': Value('bool'), 'hash_mismatches_allowed': Value('bool'), 'internal_event_ids_in_dataset': Value('bool'), 'harmful_out_of_scope_samples_in_dataset': Value('bool')}}
              because column names don't match