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Datasets:
HuggingFaceFW
/
finepdfs_100BT

Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
pretraining
smol-data
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
Community
Dataset Viewer
Auto-converted to Parquet Duplicate
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
text: string
id: string
dump: string
url: string
date: string
file_path: string
offset: int64
token_count: int64
language: string
page_average_lid: string
page_average_lid_score: double
full_doc_lid: string
full_doc_lid_score: double
per_page_languages: list<element: string>
  child 0, element: string
is_truncated: bool
extractor: string
page_ends: list<element: int64>
  child 0, element: int64
fw_edu_scores: list<element: double>
  child 0, element: double
fw_edu_v2_scores: list<element: double>
  child 0, element: double
dclm_scores: list<element: double>
  child 0, element: double
ocr_quality_scores: list<element: double>
  child 0, element: double
minhash_cluster_size: int64
duplicate_count: int64
dataset: string
to
{'text': Value('string'), 'id': Value('string'), 'dump': Value('string'), 'url': Value('string'), 'date': Value('string'), 'file_path': Value('string'), 'language': Value('string'), 'token_count': Value('int64'), 'dataset': 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 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2083, 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 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, 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/parquet/parquet.py", line 180, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 143, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              text: string
              id: string
              dump: string
              url: string
              date: string
              file_path: string
              offset: int64
              token_count: int64
              language: string
              page_average_lid: string
              page_average_lid_score: double
              full_doc_lid: string
              full_doc_lid_score: double
              per_page_languages: list<element: string>
                child 0, element: string
              is_truncated: bool
              extractor: string
              page_ends: list<element: int64>
                child 0, element: int64
              fw_edu_scores: list<element: double>
                child 0, element: double
              fw_edu_v2_scores: list<element: double>
                child 0, element: double
              dclm_scores: list<element: double>
                child 0, element: double
              ocr_quality_scores: list<element: double>
                child 0, element: double
              minhash_cluster_size: int64
              duplicate_count: int64
              dataset: string
              to
              {'text': Value('string'), 'id': Value('string'), 'dump': Value('string'), 'url': Value('string'), 'date': Value('string'), 'file_path': Value('string'), 'language': Value('string'), 'token_count': Value('int64'), 'dataset': Value('string')}
              because column names don't match

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FinePDFs 100BT

A ~100 billion token English subset of FinePDFs (eng_Latn split), created for efficient pretraining experiments.

Part of the Smol-Data collection — tried and tested mixes for strong pretraining.

Dataset Description

This dataset was created by randomly sampling from the English split of FinePDFs (~726B tokens) to produce a ~100B token subset. Sampling was performed with a fixed seed (42) and a slight 1.05× oversampling factor to account for variance.

A pre-shuffled version is available at HuggingFaceFW/finepdfs_100BT-shuffled.

How It Was Created

The dataset was generated using datatrove with the smol_data.py script. The pipeline reads from the source dataset in streaming mode, applies a SamplerFilter to downsample, and writes the result back to the Hugging Face Hub.

Usage 

from datasets import load_dataset

ds = load_dataset("HuggingFaceFW/finepdfs_100BT", split="train", streaming=True)
for sample in ds:
    print(sample["text"][:200])
    break

Citation 

@misc{niklaus2026smoldata,
      title={SmolData},
      author={Joel Niklaus and Hynek Kydl{\'\i}{\v{c}}ek},
      year={2026},
      publisher={Hugging Face},
      journal={Hugging Face repository},
      howpublished={\url{https://huggingface.co/collections/HuggingFaceFW/smol-data}}
}
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Size of the auto-converted Parquet files:
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Number of rows:
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Collection including HuggingFaceFW/finepdfs_100BT