---
license: mit
language:
- en
tags:
- regmix
pretty_name: regmix-data
size_categories:
- 10M<n<100M
---

# RegMix Data

## Dataset Description

The RegMix Data is a curated dataset derived from the Pile-Uncopyrighted, specifically designed for the RegMix paper (https://huggingface.co/papers/2407.01492). This dataset aims to facilitate the automatic identification of high-performing data mixtures for language model pre-training by formulating it as a regression task.

### Key Features:

- Size: Approximately 1TB disk space, 250B tokens
- Distribution: Follows the natural token distribution of domain examples
- Organization: Examples from different domains are separated into individual files

## Dataset Structure

The dataset is organized into two main directories: `train` and `valid`, each containing domain-specific JSONL files. The file naming convention is as follows:

```
[domain]-[identifier]-[number].jsonl
```

For example: `arxiv-10-74305611.jsonl`

### Domains Included:

arxiv, gutenberg_pg_19, pubmed_central, dm_mathematics, hackernews, stackexchange, enron_emails, nih_exporter, ubuntu_irc, europarl, philpapers, uspto_backgrounds, freelaw, pile_cc, wikipedia_en, github, pubmed_abstracts

## Usage

We recommend downloading the entire dataset snapshot instead of using the traditional `load_dataset` function, as the RegMix code is integrated with the [TinyLlama framework](https://github.com/jzhang38/TinyLlama).

To download the dataset:

```python
from huggingface_hub import snapshot_download

LOCAL_DIR = "regmix-data"
snapshot_download(repo_id="sail/regmix-data", 
                  repo_type='dataset',
                  local_dir=LOCAL_DIR,
                  local_dir_use_symlinks=False)
```

This will download the entire snapshot, containing 34 JSON line files (17 for train, and 17 for valid), to your specified local directory.

## Data Preprocessing

Our [code](https://github.com/sail-sg/regmix) will preprocess these domain files into binary format with domain prefixes. It allows for random sampling of the dataset using user-defined data mixtures (i.e., domain weights).

## Acknowledgements

We extend our gratitude to the creators of the [Pile-Uncopyrighted dataset](https://huggingface.co/datasets/monology/pile-uncopyrighted) for their efforts in removing copyrighted content from the original Pile dataset, making this work possible.

## Citation

If you use this dataset in your research, please cite the RegMix paper:

```
@article{liu2024regmix,
  title={RegMix: Data Mixture as Regression for Language Model Pre-training},
  author={Liu, Qian and Zheng, Xiaosen and Muennighoff, Niklas and Zeng, Guangtao and Dou, Longxu and Pang, Tianyu and Jiang, Jing and Lin, Min},
  journal={arXiv preprint arXiv:2407.01492},
  year={2024}
}
```

For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492).