project_gutenberg_filtered

# Project Gutenberg

## Description
[Project Gutenberg](https://www.gutenberg.org) is an online collection of over 75,000 digitized books available as plain text.
We use all books that are 1) English and 2) marked as in the Public Domain according to the provided metadata.
Additionally, we include any books that are part of the [PG19](https://huggingface.co/datasets/deepmind/pg19) dataset, which only includes books that are over 100 years old.
Minimal preprocessing is applied to remove the Project Gutenberg header and footers, but many scanned books include preamble information about who digitized them.

## Dataset Statistics
| Documents | UTF-8 GB |
|-----------|----------|
| 55,454 | 20.1 |

## 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](https://huggingface.co/papers/2506.05209)).
If you believe you have found an instance of incorrect licensing in this dataset, please [start a discussion](https://github.com/r-three/common-pile/discussions/new) on this repository.

## Other Versions
This is the "filtered" version of the Project Gutenberg dataset.
If you are looking for the raw version, you can find it [here](https://huggingface.co/datasets/common-pile/project_gutenberg_raw).

## Citation
If you use this dataset, please cite:
```bibtex
@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}
}
```

```bibtex
@article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year = {2019},
}
```

# 古腾堡计划（Project Gutenberg）

## 描述
[古腾堡计划（Project Gutenberg）](https://www.gutenberg.org) 是一个在线馆藏库，收录超75000本可通过纯文本格式获取的数字化图书。
本数据集遴选所有符合以下条件的图书：1）为英文图书；2）根据其元数据标注为处于公有领域（Public Domain）范畴。
此外，本数据集还纳入了[PG19](https://huggingface.co/datasets/deepmind/pg19)数据集所包含的全部图书——该数据集仅收录成书至今超过100年的作品。
本数据集仅进行了极简预处理：移除古腾堡计划自带的页眉与页脚，但部分扫描版图书仍保留了关于数字化者的前置说明信息。

## 数据集统计
| 文档数量 | UTF-8 存储量（GB） |
|---------|-------------------|
| 55,454 | 20.1 |

## 版权授权问题
尽管我们致力于为数据集提供完全准确的版权授权信息，但版权洗白（license laundering）与元数据失准仍可能导致我们误将错误的授权协议赋予部分文档（关于该局限性的详细讨论，请参阅[我们的论文](https://huggingface.co/papers/2506.05209)）。
若您发现本数据集存在版权授权标注错误的情况，请前往本仓库发起[讨论](https://github.com/r-three/common-pile/discussions/new)。

## 其他版本
本版本为古腾堡计划数据集的「过滤版」。若您需要原始版本，可前往[此处](https://huggingface.co/datasets/common-pile/project_gutenberg_raw)获取。

## 引用
若您使用本数据集，请引用如下文献：
bibtex
@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}
}

bibtex
@article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year={2019},
}