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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
estimated_prior_removed_pct: double
high: double
low: double
mean_log_prior_percentiles: struct<1: double, 10: double, 2.5: double, 25: double, 5: double, 50: double, 75: double, 90: double (... 39 chars omitted)
  child 0, 1: double
  child 1, 10: double
  child 2, 2.5: double
  child 3, 25: double
  child 4, 5: double
  child 5, 50: double
  child 6, 75: double
  child 7, 90: double
  child 8, 95: double
  child 9, 97.5: double
  child 10, 99: double
prior_band: list<item: double>
  child 0, item: double
sample_prior_docs: int64
sample_raw_docs_seen: int64
sample_structural_kept: int64
sample_structural_removed: struct<ocr_artifacts: int64>
  child 0, ocr_artifacts: int64
text: string
to
{'text': 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
              estimated_prior_removed_pct: double
              high: double
              low: double
              mean_log_prior_percentiles: struct<1: double, 10: double, 2.5: double, 25: double, 5: double, 50: double, 75: double, 90: double (... 39 chars omitted)
                child 0, 1: double
                child 1, 10: double
                child 2, 2.5: double
                child 3, 25: double
                child 4, 5: double
                child 5, 50: double
                child 6, 75: double
                child 7, 90: double
                child 8, 95: double
                child 9, 97.5: double
                child 10, 99: double
              prior_band: list<item: double>
                child 0, item: double
              sample_prior_docs: int64
              sample_raw_docs_seen: int64
              sample_structural_kept: int64
              sample_structural_removed: struct<ocr_artifacts: int64>
                child 0, ocr_artifacts: int64
              text: string
              to
              {'text': Value('string')}
              because column names don't match

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think-dataset-clean

Cleaned version of jbduran/think-dataset, produced with a Colab notebook based on Michael Hla's Machina Mirabilis / gpt1900 filtering approach.

Design choices:

  • Whole books are preserved as rows.
  • The post-1900 physics keyword filter is skipped because this dataset intentionally keeps texts up to the 1930s.
  • A moderate GPT-2 token log-prior band is used: p2.5-p97.5.
  • Each source shard maps to one output shard of the same basename.

Current report:

  • Shards completed: 473
  • Input documents seen: 160,263
  • Kept documents: 149,745 (93.44%)
  • Removed documents: 10,518 (6.56%)
  • Kept characters vs raw: 89.50%

Removal reasons:

  • prior_high: 3,991
  • prior_low: 3,838
  • ocr_artifacts: 2,689
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