pretty_name: Data Provenance Initiative
language:
- en
Data Provenance Initiative
Description
The Data Provenance Initiative is a digital library of supervised datasets that have been manually annotated with their source and license information [ 104, 107 ].
We leverage their tooling to filter HuggingFace datasets, based on a range of criteria, including their licenses.
Specifically, we filter the data according to these criteria: contains English language or code data, the text is not model-generated, the dataset’s audit yielded a open license and the original sources of the data are only from recognized public domain sources.
Per-document license information is available in the license entry of the metadata field of each example.
Code for collecting, processing, and preparing this dataset is available in the common-pile GitHub repo.
Dataset Statistics
| Documents | UTF-8 GB |
|---|---|
| 9,688,211 | 7 |
License Issues
While we aim to produce datasets with completely accurate licensing information, license laundering and inaccurate metadata can cause us to erroneously assign the incorrect license to some documents (for further discussion of this limitation, please see our paper). If you believe you have found an instance of incorrect licensing in this dataset, please start a discussion on this repository.
Other Versions
This is the "raw" version of the Data Provenance Initiative dataset. If you are looking for the filtered version used to train Comma v0.1, you can find it here.
Citation
If you use this dataset, please cite:
@article{kandpal2025common,
title={{The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text}},
author={Nikhil Kandpal and Brian Lester and Colin Raffel and Sebastian Majstorovic and Stella Biderman and Baber Abbasi and Luca Soldaini and Enrico Shippole and A. Feder Cooper and Aviya Skowron and Shayne Longpre and Lintang Sutawika and Alon Albalak and Zhenlin Xu and Guilherme Penedo and Loubna Ben and Elie Bakouch and John David and Honglu Fan and Dashiell Stander and Guangyu Song and Aaron Gokaslan and John Kirchenbauer and Tom Goldstein and Brian R and Bhavya Kailkhura and Tyler Murray},
journal={arXiv preprint},
year={2025}
}