| --- |
| license: odc-by |
| language: |
| - en |
| pretty_name: FinePDFs-Edu — English, Pre-tokenized & Length-Bucketed (for continuous pretraining) |
| size_categories: |
| - 10M<n<100M |
| source_datasets: |
| - extended|HuggingFaceFW/finepdfs-edu |
| task_categories: |
| - text-generation |
| tags: |
| - pretraining |
| - continual-pretraining |
| - continuous-pretraining |
| - pre-tokenized |
| - long-context |
| - pdf |
| - educational |
| viewer: false |
| --- |
| |
| # FinePDFs-Edu — English, Pre-tokenized & Length-Bucketed |
|
|
| A **high-quality, pre-tokenized, length-bucketed** derivative of |
| [`HuggingFaceFW/finepdfs-edu`](https://huggingface.co/datasets/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`](https://huggingface.co/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](#license) and [Citation](#citation). |
|
|
| --- |
|
|
| ## At a glance |
|
|
| | | | |
| |---|---| |
| | **Source dataset** | [`HuggingFaceFW/finepdfs-edu`](https://huggingface.co/datasets/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`](https://huggingface.co/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](https://opendatacommons.org/licenses/by/1-0/) + [Common Crawl ToU](https://commoncrawl.org/terms-of-use) | |
|
|
| --- |
|
|
| ## 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](#data-removal-opt-out--pii). |
|
|
| ### 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](#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: |
|
|
| 1. **Load** `HuggingFaceFW/finepdfs-edu`, config **`eng_Latn`**, split `train` (23,023,372 documents). |
| 2. **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` |
| 3. **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. |
| 4. **Bucket** each document by its final token count (text tokens + EOS) into length buckets. |
| 5. **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: |
|
|
| ```python |
| 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) |
|
|
| ```python |
| 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) |
|
|
| ```python |
| 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: |
| |
| 1. **Attribute** the source — credit **FinePDFs-Edu / Hugging Face** and preserve their |
| [citation](#citation). |
| 2. **Preserve notices** — keep the ODC-By license and this attribution intact in any further |
| redistribution, and link or include the license text. |
| 3. **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](https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu) 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](https://huggingface.co/datasets/tturing/finepdfs-edu-32/discussions). 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): |
| |
| ```bibtex |
| @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: |
|
|
| ```bibtex |
| @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. |
|
|
