Datasets:

Malavnaik
/

finfacts

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 342, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 838, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xce in position 7: invalid continuation byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4645, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2866, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 345, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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FinFacts: SEC fundamentals eval set and 10-K chunks

Built by FinFacts, a pipeline that turns SEC EDGAR XBRL filings for the S&P 500 into certified, point-in-time-correct fundamentals. Snapshot date: 2026-10-24.

What's inside

Config	Rows	What it is
`eval`	150	Natural-language questions with gold SQL and gold answers
`financials`	600,748	Current certified value per company × metric × fiscal period
`financials_history`	658,646	Every value over time (`version_no` 1 = as first reported)
`companies`	500	Companies, tickers, GICS sector, fiscal year end
`metrics`	21	Metric definitions
`tenk_chunks`	87,082	10-K Item 1A (Risk Factors) and Item 7 (MD&A), FY2024+, ~600-word chunks with `BAAI/bge-small-en-v1.5` embeddings

Eval set

110 templated questions: single values, quarters, growth, margins, sector totals, rankings, as-originally-reported and restatement counts.
40 hand-written hard questions: fiscal-calendar traps (Walmart's FY2025 ends in January 2025; J&J's 52/53-week years), bank revenue, a company that re-registered under a new CIK (ExxonMobil), derived Q4s, medians, CAGR, top-N and restatements.
Each row has question, gold_sql (DuckDB, against the tables in this dataset loaded as schema semantic), gold_answer, answer_type (number scored within 0.5%, integer exact, ticker any of the company's tickers, ticker_set same companies in any order, text exact) and category.
Verification: 15 answers (10%) were cross-checked against SEC's own companyconcept API, a different access path from the bulk files the pipeline uses. Result: 15/15 match (eval/verification.csv).

Text-to-SQL baseline

Model	Before (raw XBRL facts)	After (FinFacts semantic layer)
`anthropic/claude-haiku-5.5`	67.3%	100.0%

Execution accuracy on all questions: the same model, prompt rules and one repair attempt; only the schema differs (raw SEC facts vs the certified tables + metric definitions).

Point-in-time

financials_history keeps every version of every value with valid_from / valid_to (SEC acceptance dates). "What was known on 2021-06-01?" is valid_from <= '2021-06-01' and (valid_to is null or valid_to > '2021-06-01').

Sources and licence

Financial facts and 10-K text: SEC EDGAR (public data; accessed under SEC's fair-access policy). Company list: S&P 500 constituents from datasets/s-and-p-500-companies (ODC-PDDL). "S&P 500" is a trademark of S&P Dow Jones Indices; this dataset is not affiliated with or endorsed by S&P.
The questions, gold SQL, derived tables and documentation are released under CC BY 4.0.
Not investment advice. Values come from companies' XBRL tags and FinFacts' documented mapping rules; see the concept mapping for known limitations.

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