HuggingFaceFW/fineweb_100BT-shuffled

---
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: language_score
dtype: float64
- name: token_count
dtype: int64
- name: score
dtype: float64
- name: int_score
dtype: int64
- name: dataset
dtype: string
splits:
- name: train
num_examples: 160677091
license: odc-by
language:
- en
size_categories:
- 100M<n<1B
tags:
- pretraining
- smol-data
pretty_name: FineWeb 100BT (Shuffled)
---

# FineWeb 100BT (Shuffled)

A globally shuffled version of [HuggingFaceFW/fineweb_100BT](https://huggingface.co/datasets/HuggingFaceFW/fineweb_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 [fineweb_100BT](https://huggingface.co/datasets/HuggingFaceFW/fineweb_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/fineweb_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}}
}
```