Datasets:

vasanth009
/

macwispr-polish-data

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
raw: string
tags: list<item: string>
  child 0, item: string
origin: string
gold: string
to
{'id': Value('string'), 'tags': List(Value('string')), 'raw': Value('string')}
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
              id: string
              raw: string
              tags: list<item: string>
                child 0, item: string
              origin: string
              gold: string
              to
              {'id': Value('string'), 'tags': List(Value('string')), 'raw': Value('string')}
              because column names don't match

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MacWispr Polish — training & eval datasets

The complete open dataset behind MacWispr's on-device dictation polish model (Qwen3.5-0.8B post-trained to turn raw speech-to-text into clean, structured writing). Training pipeline and verifier live in the MacWispr repo.

Path	Rows	What it is
`sft/train.jsonl` (+valid/test)	3,011 / 276 / 173	Main SFT pool. `{"text": "### Input:\n<raw>\n\n### Output:\n<gold>"}`
`synthetic/synth_hard.jsonl`	311	Synthetic hard-category examples (multi-list, numbered, mixed styles, checklist) generated with Grok, validated by the rule-based `polish_verifier`, deduped vs all other pools. `{"raw", "gold", "tags", "source"}`
`eval/ood_eval_set.jsonl`	40	Out-of-distribution eval suite (held out from all training). `{"id", "raw", "tags", ...}`
`dpo/dpo_prompts.jsonl`	220	Prompts + golds used to build DPO preference pairs
`results/`	—	Benchmark vs Claude Sonnet (same suite, same scorer), incl. per-case outputs

Task

Input: raw ASR transcript with disfluencies and a spoken formatting request. Output: cleaned text with the requested structure (bullets / 1. numbered / - [ ] checklists / multiple labelled lists / email), fillers removed, self-corrections applied, questions preserved as questions (never answered).

Benchmark snapshot (2026-07-21)

40-case OOD suite, shared rule-based scorer:

System	Pass	Mean latency
MacWispr local 0.8B (4-bit, on-device)	23/40 (57.5%)	191 ms
Claude Sonnet (cloud)	25/40 (62.5%)	4,407 ms

Provenance & license

Synthetic and curated data created for this project (no user dictations — MacWispr never collects transcripts). Grok was used to draft the synthetic examples; every gold is machine-validated by the open verifier. MIT.

Downloads last month: 35

Total file size:

2.56 MB

MacWispr Polish — training & eval datasets

Contents

Task

Benchmark snapshot (2026-07-21)

Provenance & license