README.md

---
dataset_info:
  features:
    - name: text
      dtype: string
    - name: id
      dtype: string
    - name: dump
      dtype: string
    - name: url
      dtype: string
    - name: date
      dtype: string
    - name: file_path
      dtype: string
    - name: language
      dtype: string
    - name: token_count
      dtype: int64
    - name: dataset
      dtype: string
  splits:
    - name: train
      num_examples: 14640807
license: odc-by
language:
  - en
size_categories:
  - 10M<n<100M
tags:
  - pretraining
  - smol-data
pretty_name: FinePDFs 100BT (Shuffled)
---

# FinePDFs 100BT (Shuffled)

A globally shuffled version of [HuggingFaceFW/finepdfs_100BT](https://huggingface.co/datasets/HuggingFaceFW/finepdfs_100BT).

Part of the [Smol-Data](https://huggingface.co/collections/HuggingFaceFW/smol-data) collection — tried and tested mixes for strong pretraining.

## Dataset Description

This dataset contains the same ~100B tokens as [finepdfs_100BT](https://huggingface.co/datasets/HuggingFaceFW/finepdfs_100BT) but with all documents globally shuffled (seed=42). Use this version when you need randomized document ordering for pretraining.

## How It Was Created

The unshuffled dataset was loaded into memory, shuffled with `dataset.shuffle(seed=42)`, and re-uploaded with 100 shards. See the [smol_data.py](https://github.com/huggingface/datatrove/blob/main/examples/smol_data.py) script for details.

## Usage

```python
from datasets import load_dataset

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

## Citation

```bibtex
@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}}
}
```