A high-quality, pre-tokenized, length-bucketed derivative of HuggingFaceFW/finepdfs-edu, prepared as a drop-in corpus for distributed continuous / continual pretraining (CPT).

The source documents are filtered to confident English, tokenized once with the jet-ai/Jet-Nemotron-2B tokenizer, and written to flat uint32 token shards that are partitioned by sequence length so that training jobs can pull short or long sequences on demand (e.g. length-based batching, curriculum, or long-context stages) without re-tokenizing or re-scanning the corpus.

Attribution. This dataset is a derivative database of FinePDFs-Edu by Hugging Face (Hynek Kydlíček, Guilherme Penedo, Leandro von Werra). It is redistributed under the same Open Data Commons Attribution License (ODC-By) v1.0, and use remains subject to the Common Crawl Terms of Use. See License and Citation.

At a glance


Source dataset	`HuggingFaceFW/finepdfs-edu`, config `eng_Latn`, split `train`
Language	English (`eng_Latn`), LID-confident only
Content	Token IDs only (`uint32`) — no raw text is stored
Tokenizer	`jet-ai/Jet-Nemotron-2B` (Qwen2.5-family vocab, size 151,936)
Documents kept	18,198,668 (of 23,023,372 raw `eng_Latn` docs → 79.0% retained)
Total tokens	≈ 90.81 billion (`90,809,325,729`), mean ≈ 4,990 tokens/doc
On-disk size	≈ 340 GiB
Layout	10 length buckets × 32 partitions (`p00`–`p31`), rolling ≤ ~1.5 GB shards
License	ODC-By v1.0 + Common Crawl ToU

Intended use

Primary: distributed continuous / continual pretraining and mid-training of LLMs on a clean, educational-quality English corpus that is already tokenized and length-organized.
Length bucketing makes it cheap to:
- build fixed-length packed batches with minimal padding waste,
- run long-context training stages by drawing from the 32768/65536/131072/262144/beyond buckets,
- apply length-based curricula or per-bucket mixing weights.
The bundled processed_spec.txt maps every stored document back to its original FinePDFs-Edu id, enabling deduplication, provenance, joins back to source metadata, and — importantly — honoring removal / opt-out requests.

Out of scope / limitations

This is not a standard datasets-loadable dataset and the Dataset Viewer is disabled — the files are raw token streams (see File format), not Parquet/Arrow.
Token IDs are specific to the jet-ai/Jet-Nemotron-2B tokenizer. They are only meaningful with that tokenizer; a different tokenizer will decode to garbage.
Only English is included; the other 68 FinePDFs-Edu languages are excluded here.
Educational-quality filtering and PDF extraction are inherited from the source and carry the source's known limitations (OCR/extraction noise, classifier imperfections, residual PII — see below).

How the data was produced

The corpus was produced by the following reproducible pipeline:

Load HuggingFaceFW/finepdfs-edu, config eng_Latn, split train (23,023,372 documents).
Filter to confident English (all conditions required):
- language == "eng_Latn"
- page_average_lid == "eng_Latn"
- page_average_lid_score >= 0.90 (LID confidence threshold)
- token_count >= 1
Tokenize each surviving document with AutoTokenizer.from_pretrained("jet-ai/Jet-Nemotron-2B", trust_remote_code=True) using add_special_tokens=False, then append a single EOS token (151643, <|endoftext|>). No BOS token is added; no padding is stored.
Bucket each document by its final token count (text tokens + EOS) into length buckets.
Shard & write as raw little-endian uint32 bytes into 32 partitions, rolling to a new file at ~1.5 GB. Documents within each shard are concatenated and separated only by their trailing EOS.

Statistics

Retained 18,198,668 documents / ≈ 90.81B tokens from 23,023,372 raw eng_Latn documents.

Bucket	Sequence length (tokens, inclusive)	Documents
`1024`	1 – 1,024	7,811,084
`2048`	1,025 – 2,048	3,557,339
`4096`	2,049 – 4,096	2,536,902
`8192`	4,097 – 8,192	2,150,060
`16384`	8,193 – 16,384	1,192,685
`32768`	16,385 – 32,768	560,185
`65536`	32,769 – 65,536	235,401
`131072`	65,537 – 131,072	98,452
`262144`	131,073 – 262,144	39,142
`beyond`	> 262,144	17,418
Total		18,198,668

A bucket named N holds documents whose token length is ≤ N and > the previous bound.

Repository layout

finepdfs-edu-32/
├── README.md                # this dataset card
├── dataset_stats.txt        # raw count, LID threshold, per-bucket counts
├── processed_spec.txt       # per-document manifest (see schema below)
├── 1024/    p00_00000.npy … p31_00000.npy
├── 2048/    p00_00000.npy … p31_00000.npy
├── 4096/    …
├── 8192/    …
├── 16384/   p00_00000.npy, p00_00001.npy, …   # larger buckets roll into multiple files per partition
├── 32768/   …
├── 65536/   …
├── 131072/  …
├── 262144/  …
└── beyond/  p00_00000.npy … p31_00000.npy

pXX = partition index (00–31); _NNNNN = rolling shard index within that (bucket, partition), a new file starting every ~1.5 GB.
The .npy extension is a naming convention only — these are raw binary token dumps with no NumPy header. Do not use np.load(); use np.fromfile / np.memmap (below).

File format

dtype: little-endian uint32 (4 bytes/token) — required because the vocabulary (151,936) exceeds uint16.
structure: documents concatenated back-to-back; each document ends with exactly one EOS token (151643). No BOS, no padding, no per-document header.
To recover document boundaries, split on the EOS id.

Usage

1. Stream the flat token stream (typical for CPT)

For continuous pretraining you usually just want a long stream of tokens to pack into fixed-length sequences. The shards are already shuffled and EOS-separated, so you can consume them directly:

import numpy as np

DTYPE = np.uint32
tokens = np.memmap("1024/p00_00000.npy", dtype=DTYPE, mode="r")  # zero-copy, lazy
# feed `tokens` into your packing / sequence-chunking pipeline

2. Recover individual documents (split on EOS)

import numpy as np

DTYPE  = np.uint32
EOS_ID = 151643  # <|endoftext|> for the jet-ai/Jet-Nemotron-2B tokenizer

tokens  = np.memmap("4096/p00_00000.npy", dtype=DTYPE, mode="r")
eos_pos = np.where(tokens == EOS_ID)[0]
starts  = np.concatenate(([0], eos_pos[:-1] + 1))
docs    = [tokens[s:e] for s, e in zip(starts, eos_pos)]  # each doc EXCLUDES its trailing EOS
# use tokens[s:e + 1] instead if you want to keep the EOS

3. Decode back to text (sanity check)

from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("jet-ai/Jet-Nemotron-2B", trust_remote_code=True)
print(tok.decode(docs[0], skip_special_tokens=True))

`processed_spec.txt` schema

Pipe-delimited, one header line then one row per stored document:

rel_path | original_id | processed_token_count
1024/p00_00000.npy | <urn:uuid:4ff9ad54-9533-4ec5-bb15-e2f82cbe63f4> | 691

rel_path — bucket/shard file the document was written to.
original_id — the document's id in HuggingFaceFW/finepdfs-edu. Use it to join back to source metadata (url, date, fw_edu_scores, …) and to action removal requests.
processed_token_count — token count including the appended EOS (this determines the bucket).

License

This derivative is released under the Open Data Commons Attribution License (ODC-By) v1.0, matching the source. Use of the underlying content also remains subject to the Common Crawl Terms of Use.

ODC-By v1.0 license text: https://opendatacommons.org/licenses/by/1-0/
Common Crawl Terms of Use: https://commoncrawl.org/terms-of-use

ODC-By is a permissive attribution license: it permits copying, modification, creation of derivative databases, and redistribution. There is no share-alike obligation. To comply you must:

Attribute the source — credit FinePDFs-Edu / Hugging Face and preserve their citation.
Preserve notices — keep the ODC-By license and this attribution intact in any further redistribution, and link or include the license text.
Honor removals — propagate opt-out / PII-removal requests downstream (see below).

Because there is no share-alike clause, a downstream user may relicense their own further derivative; keeping odc-by is the simplest compliant choice and is what this repository does.

Data removal, opt-out & PII

This corpus inherits the personal-data characteristics of FinePDFs-Edu / Common Crawl and may contain residual personally identifiable information despite upstream anonymization. We honor removal and opt-out requests and propagate them downstream.

To remove content from the source (FinePDFs / FinePDFs-Edu): Hugging Face provides a PII removal / opt-out form — usable if you find your PII, your copyrighted work, or (as a webmaster) your website in the data. See the "Personal and Sensitive Information" section of the FinePDFs-Edu dataset card for the current form link. Removals accepted upstream should be re-pulled here.
To remove content from this derivative: open a discussion on this repository's Community tab. Because processed_spec.txt maps every stored document to its original FinePDFs-Edu id, a flagged document can be located and its tokens deleted from the affected shard(s). Please include the original_id (the <urn:uuid:…>) or the source URL.

If you build on this dataset, please carry this section forward and keep honoring the same requests.

Citation

Please cite the original FinePDFs work (keep this citation in any redistribution):

@misc{kydlicek2025finepdfs,
      title={FinePDFs},
      author={Hynek Kydl{\'\i}{\v{c}}ek and Guilherme Penedo and Leandro von Werra},
      year={2025},
      publisher = {Hugging Face},
      journal = {Hugging Face repository},
      howpublished = {\url{https://huggingface.co/datasets/HuggingFaceFW/finepdfs_edu}}
}

If you use this pre-tokenized, length-bucketed derivative specifically, you may additionally cite it as:

@misc{finepdfs_edu_pretokenized,
      title  = {FinePDFs-Edu — English, Pre-tokenized & Length-Bucketed},
      note   = {Derivative of HuggingFaceFW/finepdfs-edu (eng_Latn), tokenized with jet-ai/Jet-Nemotron-2B and bucketed by sequence length},
      year   = {2025},
      howpublished = {\url{https://huggingface.co/datasets/tturing/finepdfs-edu-32}}
}

Acknowledgements

Built on FinePDFs-Edu by Hugging Face. FinePDFs is, at release, the largest publicly available corpus sourced exclusively from PDFs (~3T tokens across ~475M documents in 1,733 languages); FinePDFs-Edu is its educational-quality subset. All credit for the underlying data collection, extraction, and filtering belongs to the FinePDFs authors and Hugging Face.

Downloads last month: 1,320

Total file size:

365 GB

FinePDFs-Edu — English, Pre-tokenized & Length-Bucketed