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.
To download the dataset:
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 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 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.