Datasets:

UraionLabs
/

FilingsBench

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
annotation: struct<adjudicated: bool, guideline_version: string, source: string, state: string>
  child 0, adjudicated: bool
  child 1, guideline_version: string
  child 2, source: string
  child 3, state: string
filing: struct<accession: string, form: string, url: string>
  child 0, accession: string
  child 1, form: string
  child 2, url: string
issuer: struct<cik: string, name: string>
  child 0, cik: string
  child 1, name: string
labels: struct<disclosure_controls_effectiveness: struct<status: string, value: string>, icfr_effectiveness: (... 212 chars omitted)
  child 0, disclosure_controls_effectiveness: struct<status: string, value: string>
      child 0, status: string
      child 1, value: string
  child 1, icfr_effectiveness: struct<status: string, value: string, reason: string>
      child 0, status: string
      child 1, value: string
      child 2, reason: string
  child 2, material_weakness_disclosed: struct<status: string, value: bool, reason: string>
      child 0, status: string
      child 1, value: bool
      child 2, reason: string
  child 3, remediation_status: struct<status: string, value: string, reason: string>
      child 0, status: string
      child 1, value: string
      child 2, reason: string
record_id: string
split: string
prediction: struct<document: struct<schema_version: string, source_url: string>, fields: struct<disclosure_contr (... 1181 chars omitted)
  child 0, document: struct<schema_version: string, source_url: string>
      child 0, s
...
ld 1, evidence: list<item: struct<concept: null, context_ref: null, end_char: int64, method: string, raw_text: strin (... 59 chars omitted)
              child 0, item: struct<concept: null, context_ref: null, end_char: int64, method: string, raw_text: string, section: (... 47 chars omitted)
                  child 0, concept: null
                  child 1, context_ref: null
                  child 2, end_char: int64
                  child 3, method: string
                  child 4, raw_text: string
                  child 5, section: string
                  child 6, source_url: string
                  child 7, start_char: int64
          child 2, reason: string
          child 3, status: string
          child 4, value: string
  child 2, telemetry: struct<inference_cost_usd: double, latency_ms: double, model: string, peak_python_memory_mb: double>
      child 0, inference_cost_usd: double
      child 1, latency_ms: double
      child 2, model: string
      child 3, peak_python_memory_mb: double
scores: struct<disclosure_controls_effectiveness: struct<correct: bool>, icfr_effectiveness: struct<correct: (... 102 chars omitted)
  child 0, disclosure_controls_effectiveness: struct<correct: bool>
      child 0, correct: bool
  child 1, icfr_effectiveness: struct<correct: bool>
      child 0, correct: bool
  child 2, material_weakness_disclosed: struct<correct: bool>
      child 0, correct: bool
  child 3, remediation_status: struct<correct: bool>
      child 0, correct: bool
to
{'prediction': {'document': {'schema_version': Value('string'), 'source_url': Value('string')}, 'fields': {'disclosure_controls_effectiveness': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('null'), 'status': Value('string'), 'value': Value('string')}, 'icfr_effectiveness': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('string'), 'status': Value('string'), 'value': Value('string')}, 'material_weakness_disclosed': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('string'), 'status': Value('string'), 'value': Value('bool')}, 'remediation_status': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('string'), 'status': Value('string'), 'value': Value('string')}}, 'telemetry': {'inference_cost_usd': Value('float64'), 'latency_ms': Value('float64'), 'model': Value('string'), 'peak_python_memory_mb': Value('float64')}}, 'record_id': Value('string'), 'scores': {'disclosure_controls_effectiveness': {'correct': Value('bool')}, 'icfr_effectiveness': {'correct': Value('bool')}, 'material_weakness_disclosed': {'correct': Value('bool')}, 'remediation_status': {'correct': Value('bool')}}, 'split': Value('string')}
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
              annotation: struct<adjudicated: bool, guideline_version: string, source: string, state: string>
                child 0, adjudicated: bool
                child 1, guideline_version: string
                child 2, source: string
                child 3, state: string
              filing: struct<accession: string, form: string, url: string>
                child 0, accession: string
                child 1, form: string
                child 2, url: string
              issuer: struct<cik: string, name: string>
                child 0, cik: string
                child 1, name: string
              labels: struct<disclosure_controls_effectiveness: struct<status: string, value: string>, icfr_effectiveness: (... 212 chars omitted)
                child 0, disclosure_controls_effectiveness: struct<status: string, value: string>
                    child 0, status: string
                    child 1, value: string
                child 1, icfr_effectiveness: struct<status: string, value: string, reason: string>
                    child 0, status: string
                    child 1, value: string
                    child 2, reason: string
                child 2, material_weakness_disclosed: struct<status: string, value: bool, reason: string>
                    child 0, status: string
                    child 1, value: bool
                    child 2, reason: string
                child 3, remediation_status: struct<status: string, value: string, reason: string>
                    child 0, status: string
                    child 1, value: string
                    child 2, reason: string
              record_id: string
              split: string
              prediction: struct<document: struct<schema_version: string, source_url: string>, fields: struct<disclosure_contr (... 1181 chars omitted)
                child 0, document: struct<schema_version: string, source_url: string>
                    child 0, s
              ...
              ld 1, evidence: list<item: struct<concept: null, context_ref: null, end_char: int64, method: string, raw_text: strin (... 59 chars omitted)
                            child 0, item: struct<concept: null, context_ref: null, end_char: int64, method: string, raw_text: string, section: (... 47 chars omitted)
                                child 0, concept: null
                                child 1, context_ref: null
                                child 2, end_char: int64
                                child 3, method: string
                                child 4, raw_text: string
                                child 5, section: string
                                child 6, source_url: string
                                child 7, start_char: int64
                        child 2, reason: string
                        child 3, status: string
                        child 4, value: string
                child 2, telemetry: struct<inference_cost_usd: double, latency_ms: double, model: string, peak_python_memory_mb: double>
                    child 0, inference_cost_usd: double
                    child 1, latency_ms: double
                    child 2, model: string
                    child 3, peak_python_memory_mb: double
              scores: struct<disclosure_controls_effectiveness: struct<correct: bool>, icfr_effectiveness: struct<correct: (... 102 chars omitted)
                child 0, disclosure_controls_effectiveness: struct<correct: bool>
                    child 0, correct: bool
                child 1, icfr_effectiveness: struct<correct: bool>
                    child 0, correct: bool
                child 2, material_weakness_disclosed: struct<correct: bool>
                    child 0, correct: bool
                child 3, remediation_status: struct<correct: bool>
                    child 0, correct: bool
              to
              {'prediction': {'document': {'schema_version': Value('string'), 'source_url': Value('string')}, 'fields': {'disclosure_controls_effectiveness': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('null'), 'status': Value('string'), 'value': Value('string')}, 'icfr_effectiveness': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('string'), 'status': Value('string'), 'value': Value('string')}, 'material_weakness_disclosed': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('string'), 'status': Value('string'), 'value': Value('bool')}, 'remediation_status': {'confidence': Value('float64'), 'evidence': List({'concept': Value('null'), 'context_ref': Value('null'), 'end_char': Value('int64'), 'method': Value('string'), 'raw_text': Value('string'), 'section': Value('string'), 'source_url': Value('string'), 'start_char': Value('int64')}), 'reason': Value('string'), 'status': Value('string'), 'value': Value('string')}}, 'telemetry': {'inference_cost_usd': Value('float64'), 'latency_ms': Value('float64'), 'model': Value('string'), 'peak_python_memory_mb': Value('float64')}}, 'record_id': Value('string'), 'scores': {'disclosure_controls_effectiveness': {'correct': Value('bool')}, 'icfr_effectiveness': {'correct': Value('bool')}, 'material_weakness_disclosed': {'correct': Value('bool')}, 'remediation_status': {'correct': Value('bool')}}, 'split': Value('string')}
              because column names don't match

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FilingsBench

An accession-linked, issuer-disjoint benchmark for auditable extraction from SEC 10-K, 10-Q, and 8-K filings. The financial-facts v0.1 slice contains 60 filings from 15 issuers and eight fields; its labels are explicitly silver SEC cross-source, not human-gold. Narrative slices use candidate_pending_independent_review until blinded review and adjudication are complete.

The repository contains provenance and labels, not copies of filing HTML. See FinStruct for builders, raw predictions, metrics, schemas, and known failures.

Financial-facts dataset SHA-256: fbac61331b715ac196e6af57dc8276c40d3819b31489cf77c7a7914bb17b5725.

Controls v0.1 candidate slice

The controls/ directory adds 12 issuer-disjoint filings and four narrative fields: ICFR effectiveness, disclosure-controls effectiveness, material-weakness presence, and remediation status. The deterministic v0.2.0 baseline matched all 48 candidate outcomes and attached exact evidence to every present prediction. Labels remain candidate_pending_independent_review.

Controls dataset SHA-256: 9de0c23ed5e971d9426b7d235b52332fb13c257829d4a09743641eef213717de.

Controls report

Form 8-K Item 4.02 v0.1 candidate slice

The non_reliance/ directory adds 14 issuer-disjoint Form 8-K primary documents and six fields: Item 4.02 paragraph variant, non-reliance scope, latest conclusion/notice date, restatement status, auditor-discussion disclosure, and controlled accounting topics. Twelve records contain Item 4.02; two are negative 8-K controls that should abstain on every field.

The deterministic v0.3.0 baseline matched all 84 candidate outcomes, attached evidence to every present prediction, and passed 82/82 normalized-document offset checks. The first run exposed an incomplete candidate label for one filing; exact evidence corrected the label without a parser change. These labels are not human-gold or a prevalence sample.

Item 4.02 dataset SHA-256: 2113ed77793a52c133d674c74c29238d684e77c3fdff1888f92d97dc5b9b594e.

The directory includes a label- and prediction-free review_packet.jsonl for blinded independent review, with SHA-256 3ea7169169698ea9155df7d90705719df46fe3e799a37e5814911643ec814aec.

The v0.3.1 reviewer kit adds review_template.csv, a spreadsheet handoff generated from the blind packet, plus its submission schema and walkthrough. FinStruct validates all 14 records and 84 field decisions, enforces controlled values and abstention rationales, and can verify exact quotes against SEC filed text before emitting normalized JSONL offsets. The template SHA-256 is ca24011b90c85204d581aefeca8a5cdbee3a239fff1cab5aed2c570540c5d027.

The v0.3.3 starter kit adds review_packet_starter.jsonl and review_template_starter.csv: four filings and 24 field decisions selected only by ascending sha256(starter-v0.1:record_id). The packet remains label- and prediction-free and uses the same validator as the full review. A validated starter is partial independent coverage, not a complete slice review or human-gold claim. The packet SHA-256 is 17954faa8388889b6ca59ecec40be39a377bd01c671e43ae5251947397299cab; the template SHA-256 is 22a9d97c1dc68559e93520fe134706750aa4f4505236baff882c13701622f8e9.

FinStruct v0.5.0 adds deterministic comparison and adjudication for two normalized submissions. It isolates status, value, and exact-evidence disagreements; requires a third distinct adjudicator; and re-verifies every present source and final quote against SEC filed text before emitting the human_gold_double_review_adjudicated state. This is promotion infrastructure only: the dataset currently contains zero independent submissions and zero human-gold records.

Item 4.02 report · Independent review walkthrough · Tutorial · v0.5.0 release

Zero-setup v0.3.2 quickstart

Open the executed notebook in Colab to install the immutable v0.3.2 wheel, verify one tagged temporal-audit filing hash, extract the six Item 4.02 fields, and round-trip all seven emitted evidence offsets. It requires no repository checkout, model, GPU, inference API, SEC credential, or private document.

The notebook reproduces installation and system behavior on unlabeled development data. It does not establish accuracy, human-gold status, post-tuning generalization, or production quality.

Item 4.02 temporal audit v0.2

The temporal/ directory adds a label-free 25-issuer audit frozen before prediction from a fixed May 1–July 27, 2026 SEC EFTS query. It contains the exact selection manifest, accession-linked records, and separate v0.3.1 baseline and v0.3.2 development-replay outputs. The records SHA-256 is 0044db83e2782d4a02f75f7b9f376274a0ea6f056129e22fb8eec441069e620e.

Accuracy is explicitly null. Maintainer inspection changed 37 field outputs across 18 filings; the final development replay emitted 141/150 fields and passed 154/154 evidence-offset checks. Because the set was used for development, it is not human-gold, an untouched post-tuning holdout, market prevalence, or production accuracy.

Temporal audit

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Total file size:

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Space using UraionLabs/FilingsBench 1

Collection including UraionLabs/FilingsBench

Auditable Financial Document Intelligence

Collection

Current Uraion Labs product surface: FinStruct and FilingsBench for evidence-linked, local-first SEC filing extraction. • 2 items • Updated 10 days ago