Datasets:

playra
/

numeric-format-catalog

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
bias: int64
bits: int64
cluster: string
e_bits: int64
gf_relation: string
id: string
m_bits: int64
name: string
phi_distance: double
s_bits: int64
source: string
standard: string
status: string
storage: string
use_case: string
count: int64
formats: list<item: struct<id: string, name: string, bits: int64, s_bits: int64, e_bits: int64, m_bits: int64 (... 161 chars omitted)
  child 0, item: struct<id: string, name: string, bits: int64, s_bits: int64, e_bits: int64, m_bits: int64, bias: dou (... 149 chars omitted)
      child 0, id: string
      child 1, name: string
      child 2, bits: int64
      child 3, s_bits: int64
      child 4, e_bits: int64
      child 5, m_bits: int64
      child 6, bias: double
      child 7, phi_distance: double
      child 8, storage: string
      child 9, cluster: string
      child 10, status: string
      child 11, standard: string
      child 12, use_case: string
      child 13, gf_relation: string
      child 14, source: string
to
{'count': Value('int64'), 'formats': List({'id': Value('string'), 'name': Value('string'), 'bits': Value('int64'), 's_bits': Value('int64'), 'e_bits': Value('int64'), 'm_bits': Value('int64'), 'bias': Value('float64'), 'phi_distance': Value('float64'), 'storage': Value('string'), 'cluster': Value('string'), 'status': Value('string'), 'standard': Value('string'), 'use_case': Value('string'), 'gf_relation': Value('string'), 'source': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              bias: int64
              bits: int64
              cluster: string
              e_bits: int64
              gf_relation: string
              id: string
              m_bits: int64
              name: string
              phi_distance: double
              s_bits: int64
              source: string
              standard: string
              status: string
              storage: string
              use_case: string
              count: int64
              formats: list<item: struct<id: string, name: string, bits: int64, s_bits: int64, e_bits: int64, m_bits: int64 (... 161 chars omitted)
                child 0, item: struct<id: string, name: string, bits: int64, s_bits: int64, e_bits: int64, m_bits: int64, bias: dou (... 149 chars omitted)
                    child 0, id: string
                    child 1, name: string
                    child 2, bits: int64
                    child 3, s_bits: int64
                    child 4, e_bits: int64
                    child 5, m_bits: int64
                    child 6, bias: double
                    child 7, phi_distance: double
                    child 8, storage: string
                    child 9, cluster: string
                    child 10, status: string
                    child 11, standard: string
                    child 12, use_case: string
                    child 13, gf_relation: string
                    child 14, source: string
              to
              {'count': Value('int64'), 'formats': List({'id': Value('string'), 'name': Value('string'), 'bits': Value('int64'), 's_bits': Value('int64'), 'e_bits': Value('int64'), 'm_bits': Value('int64'), 'bias': Value('float64'), 'phi_distance': Value('float64'), 'storage': Value('string'), 'cluster': Value('string'), 'status': Value('string'), 'standard': Value('string'), 'use_case': Value('string'), 'gf_relation': Value('string'), 'source': Value('string')})}
              because column names don't match

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numeric-format-catalog v2 (audit-disclosed)

Version: v2.0 (2026-06-10) -- supersedes v1 (count=77, withdrawn).

Count: 81 formats across 13 clusters.

Honest count-drift disclosure

This dataset is shipped with a known count-drift across four artefacts:

Source	Count	Status
`arXiv:2606.09686` Table 1 (paper #3)	84	aspirational, MXFP6 split + standalone E8M0
`specs/numeric/formats_catalog.t27` SSOT raw `// CATALOG:` lines	83	live ground truth
THIS DATASET (codegen LIVE re-run)	81	parser drops `gf512` + `gf1024` on bias-formula int() cast
`gen/numeric/formats_catalog.json` (committed in repo)	77	stale, pre-#1051 snapshot
v1 of this dataset (uploaded 2026-06-10 AM)	77	inherited stale gen

Why 81 and not 83 or 84: the codegen tools/gen_formats_catalog.py cannot evaluate bias formulas of the form 2^N-1, which are the canonical bias values for gf512 and gf1024. Those two rows are silently dropped. We ship what the codegen actually produces, not a cherry-picked number. Patch tracked in t27#1064.

Why ship 81 today instead of waiting for the patch: because v1 shipped 77 yesterday. Replacing a stale 77 with an honest 81 + full audit is closer to the truth than leaving 77 up while the patch lands.

What's in the dataset

81 row entries, each carrying: id, name, bits, s (sign bits), e (exponent bits), m (mantissa bits), bias, phi_distance, storage, cluster, status, standard, use_case, source
13 cluster groupings: IEEE754 binary, IEEE754 decimal, MLLowPrecision, GoldenFloat, Posit/Unum III, OCP MX, LNS, IntegerFixed, HistoricalVendor, Theoretical, Compression, Extended, QuantTuned
Source SSOT: gHashTag/t27 specs/numeric/formats_catalog.t27 HEAD 6ecad30
Regen output SHA-256: 546ba6fff1daec0b64483f599778ab63a9410405592c2e9dbc9d90dbb63bc48a

Known gaps vs paper #3 (arXiv:2606.09686)

Cluster	Paper says	This dataset	Delta
GoldenFloat	16	16	0 (both pre-#1051; current SSOT has 22, of which gen lifts 20)
OCP MX	5	3	-2 (no MXFP6 E2M3/E3M2 split; no standalone E8M0 block)
MLLowPrecision	8	7	-1 (NF4 placement)
Total	84	81	-3

Reconciliation

t27#1064 -- tracking issue, five-step plan: parser patch -> regen -> CI invariant -> HF re-upload v3 -> arXiv erratum if needed
Expected v3 ship count after parser patch: 83
Expected v4 ship count after MXFP6/E8M0 reconciliation with paper: 84 (or paper erratum to whatever codegen produces)

Citation

If you use this dataset, please cite the paper it accompanies:

@misc{vasilev2026numeric,
  title={An 84-Format Numeric Catalog with Bit-Exact Conformance Vectors},
  author={Vasilev, Dmitrii},
  year={2026},
  eprint={2606.09686},
  archivePrefix={arXiv},
  primaryClass={cs.MS}
}

Note: paper claims 84; this dataset ships 81 due to the parser bug. See "Honest count-drift disclosure" above.

Provenance

Uploader: playra (Dmitrii Vasilev, ORCID 0009-0008-4294-6159)
Affiliation: Trinity S3AI
Repo: gHashTag/t27
License: CC BY 4.0
Generated: 2026-06-10 +07 from HEAD 6ecad30

Downloads last month: 7

Total file size:

80.2 kB

Paper for playra/numeric-format-catalog

An 84-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats

Paper • 2606.09686 • Published 3 days ago