Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
filing: string
n_facts: int64
n_distinct_concepts: int64
core_present: list<item: string>
child 0, item: string
core_concept_exposure: list<item: struct<concept: string, filings: int64, exposure: double>>
child 0, item: struct<concept: string, filings: int64, exposure: double>
child 0, concept: string
child 1, filings: int64
child 2, exposure: double
top_concepts: list<item: struct<concept: string, filings: int64, coverage: double>>
child 0, item: struct<concept: string, filings: int64, coverage: double>
child 0, concept: string
child 1, filings: int64
child 2, coverage: double
meta: struct<source: string, source_url: string, licence: string, filings_parsed: int64, median_facts_per_ (... 45 chars omitted)
child 0, source: string
child 1, source_url: string
child 2, licence: string
child 3, filings_parsed: int64
child 4, median_facts_per_filing: int64
child 5, distinct_concepts_seen: int64
to
{'meta': {'source': Value('string'), 'source_url': Value('string'), 'licence': Value('string'), 'filings_parsed': Value('int64'), 'median_facts_per_filing': Value('int64'), 'distinct_concepts_seen': Value('int64')}, 'core_concept_exposure': List({'concept': Value('string'), 'filings': Value('int64'), 'exposure': Value('float64')}), 'top_concepts': List({'concept': Value('string'), 'filings': Value('int64'), 'coverage': Value('float64')})}
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
filing: string
n_facts: int64
n_distinct_concepts: int64
core_present: list<item: string>
child 0, item: string
core_concept_exposure: list<item: struct<concept: string, filings: int64, exposure: double>>
child 0, item: struct<concept: string, filings: int64, exposure: double>
child 0, concept: string
child 1, filings: int64
child 2, exposure: double
top_concepts: list<item: struct<concept: string, filings: int64, coverage: double>>
child 0, item: struct<concept: string, filings: int64, coverage: double>
child 0, concept: string
child 1, filings: int64
child 2, coverage: double
meta: struct<source: string, source_url: string, licence: string, filings_parsed: int64, median_facts_per_ (... 45 chars omitted)
child 0, source: string
child 1, source_url: string
child 2, licence: string
child 3, filings_parsed: int64
child 4, median_facts_per_filing: int64
child 5, distinct_concepts_seen: int64
to
{'meta': {'source': Value('string'), 'source_url': Value('string'), 'licence': Value('string'), 'filings_parsed': Value('int64'), 'median_facts_per_filing': Value('int64'), 'distinct_concepts_seen': Value('int64')}, 'core_concept_exposure': List({'concept': Value('string'), 'filings': Value('int64'), 'exposure': Value('float64')}), 'top_concepts': List({'concept': Value('string'), 'filings': Value('int64'), 'coverage': Value('float64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
iXBRL Structured-Disclosure Benchmark
How machine-readable are UK company accounts, really? This benchmark parses a full day of Companies House accounts filings (Inline XBRL) and measures what fraction of real filings expose each core accounting concept as a structured, machine-readable fact, no OCR or layout heuristics required.
| Filings parsed | 8,856 |
| Distinct taxonomy concepts seen | 899 |
| Median tagged facts per filing | 49 |
| Source | Companies House daily accounts bulk (iXBRL) |
| Licence | Open Government Licence v3.0 |
The headline
Core balance-sheet concepts are exposed as structured facts in the large majority of filings: Equity in 97.2%, Net assets in 86.6%, Creditors in 75.6%, Total assets less current liabilities in 73.3%. This is the quantity any claim that "structured data beats PDF for AI-driven reporting" ultimately rests on: not that the data could be structured, but that in the real filed population it already is, at a measurable rate, per concept.
Files
data/benchmark.json— concept-coverage table + core-concept exposuredata/filings.jsonl— per-filing concept counts and core concepts presentscripts/extract.py— reproducible parser over the daily bulk archive
Reproduce
curl -L -o data/accounts.zip \
"https://download.companieshouse.gov.uk/Accounts_Bulk_Data-2026-07-08.zip"
python3 scripts/extract.py
Contains public sector information from Companies House licensed under the Open Government Licence v3.0. Independent, self-initiated open research by Tesseract Academy.
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