README.md

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- falcon
- refinedweb
- 1B
- parquet
- web-refined
- text-generation
- clean-web-corpus
- llm-pretrain
- domain-agnostic
- sentence-quality-filtered
- huggingface-refinedweb
size_categories:
- 1B<n<10B
---

# Falcon RefinedWeb 1B

## Dataset Description
This is a **1.01 Billion token** subset of the [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) dataset. It was created by streaming the dataset with a large shuffle buffer to ensure a random, representative sample of the web data.

## Motivation
RefinedWeb is a high-quality filtered web dataset, but the full version is massive. This 1B token slice provides a perfect testbed for evaluating model architecture changes or for use in curriculum learning experiments.

## Dataset Details
- **Total Tokens:** 1,005,000,041 (~1.01B)
- **Source:** [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
- **Method:** Streamed with shuffle buffer (size=5000).
- **Format:** Parquet (Snappy compression)
- **Producer:** Kris Bailey (kris@krisbailey.com)

## Usage

```python
from datasets import load_dataset

ds = load_dataset("krisbailey/falcon-refinedweb-1B", split="train")
print(ds[0])
```

## Subsets & Slicing
Since this dataset was randomly shuffled during creation, you can safely slice it to get smaller, representative datasets (e.g., for scaling laws experiments) without needing to download the full dataset.

```python
# 100M Token Subset (approx 10%)
ds_100m = load_dataset("krisbailey/falcon-refinedweb-1B", split="train[:10%]")

# 500M Token Subset (approx 50%)
ds_500m = load_dataset("krisbailey/falcon-refinedweb-1B", split="train[:50%]")
```

## Citation
```bibtex
@article{penedo2023refinedweb,
  title={The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only},
  author={Penedo, Guilherme and Malartic, Quentin and Hesslow, Daniel and Cojocaru, Ruxandra and Cappelli, Alessandro and Alobeidli, Hamza and Pannier, Baptiste and Almazrouei, Ebtesam and Launay, Julien},
  journal={arXiv preprint arXiv:2306.01116},
  year={2023}
}
```