cccc_filtered

# Creative Commons Common Crawl

## Description
This dataset contains text from 52 Common Crawl snapshots, covering about half of Common Crawl snapshots available to date and covering all years of operations of Common Crawl up to 2024.
We found a higher level of duplication across this collection, suggesting that including more snapshots would lead to a modest increase in total token yield.
From these snapshots, we extract HTML content using [FastWarc](https://arxiv.org/abs/2112.03103).
Then, using a regular expression adapted from [the C4Corpus project](https://aclanthology.org/L16-1146/).
To ensure license accuracy, we manually verified the top 1000 domains by content volume, retaining only the 537 domains with confirmed licenses where the Creative Commons designation applied to the all text content rather than embedded media or a subset of the text on the domain.
As an additional check, we did a second round of annotations with the assistance of OpenAI's o3 model. Specifically, we instructed the model to examine each web domain and identify the ones that were openly licensed. We then had a second team manually annotate the cases where the AI does not approve of the domain but the original human auditor did. This resulted in **todo** domains being removed.

We extract the main content of these documents and remove boilerplate using [Resiliparse](https://github.com/chatnoir-eu/chatnoir-resiliparse).
We perform URL-level exact deduplication and use Bloom filters to remove near-duplicates with 80% ngram overlap.
We also employ rule-based filters matching [Dolma](https://arxiv.org/abs/2402.00159);
namely, we use [C4-derived heuristics](https://arxiv.org/abs/1910.10683) to filter pages containing Javascript, Lorem Ipsum, and curly braces {}.
We also apply all [Gopher rules](https://arxiv.org/abs/2112.11446) to remove low-quality pages.
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](https://github.com/r-three/common-pile).

## Dataset Statistics
| Documents | UTF-8 GB |
|--------------|-----------|
| 6,852,137 | 58 |

## 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.
This dataset has been updated to remove instances of incorrect licensing.
If you require the exact version that Comma v0.1 was trained on for non-commercial research purposes, please [start a discussion](https://github.com/r-three/common-pile/discussions/new) on this repository.

## Other Versions
This is the "filtered" version of Creative Commons Common Crawl. If you are looking for the raw version, you can find it [here](https://huggingface.co/datasets/common-pile/cccc_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}
}
```

# 知识共享公共爬虫（Creative Commons Common Crawl）

## 数据集描述
本数据集包含来自52个公共爬虫（Common Crawl）快照的文本，覆盖了目前可用公共爬虫快照的约一半，且涵盖了公共爬虫截至2024年的所有运营年份。
我们在该数据集集合中发现了较高程度的重复现象，这表明增加更多快照将适度提升总Token产出量。
我们通过[FastWarc](https://arxiv.org/abs/2112.03103)提取这些快照中的HTML内容。
随后，我们采用改编自[C4语料库项目（C4Corpus）](https://aclanthology.org/L16-1146/)的正则表达式进行处理。
为确保许可证信息准确，我们按内容体量对前1000个域名进行了人工审核，仅保留了537个已确认许可证的域名——这些域名的知识共享（Creative Commons）标识适用于全部文本内容，而非嵌入媒体或该域名下的部分文本。
作为额外校验，我们借助OpenAI的o3模型进行了第二轮标注。具体而言，我们要求该模型检查每个网络域名，并识别出采用开放许可证的域名。随后，我们安排第二组人工审核团队对“AI判定不符合要求但原始人工审核者认为符合”的案例进行标注，最终移除了**todo**个域名。

我们提取这些文档的主体内容，并使用[Resiliparse](https://github.com/chatnoir-eu/chatnoir-resiliparse)去除冗余格式文本。
我们执行URL级别的精确去重，并使用布隆过滤器（Bloom filter）移除ngram重叠率达80%的近似重复内容。
我们还采用了匹配[Dolma](https://arxiv.org/abs/2402.00159)的基于规则的过滤器；具体来说，我们使用[源自C4的启发式规则（C4-derived heuristics）](https://arxiv.org/abs/1910.10683)来过滤包含JavaScript、乱数假文（Lorem Ipsum）以及大括号{}的页面。
我们还应用了所有[Gopher规则](https://arxiv.org/abs/2112.11446)以移除低质量页面。
每个样本的元数据（metadata）字段中的`license`条目包含了单文档的许可证信息。
用于收集、处理和制备本数据集的代码可在[common-pile GitHub仓库](https://github.com/r-three/common-pile)中获取。

## 数据集统计
| 文档数量 | UTF-8 大小（GB） |
|--------------|-------------------|
| 6,852,137 | 58 |

## 许可证相关问题
尽管我们致力于生成许可证信息完全准确的数据集，但许可证洗白和元数据不准确可能导致我们错误地为部分文档分配了错误的许可证（关于该局限性的进一步讨论，请参阅[我们的论文](https://huggingface.co/papers/2506.05209)）。
如果您认为本数据集中存在许可证标注错误的案例，请在本仓库中[发起讨论](https://github.com/r-three/common-pile/discussions/new)。
本数据集已更新，移除了存在许可证标注错误的样本。
如果您需要用于非商业研究目的的、Comma v0.1训练所用的精确版本数据集，请在本仓库中[发起讨论](https://github.com/r-three/common-pile/discussions/new)。

## 其他版本
本版本为知识共享公共爬虫的“过滤后”版本。若您需要原始版本，可在此处获取：[https://huggingface.co/datasets/common-pile/cccc_raw](https://huggingface.co/datasets/common-pile/cccc_raw)。

## 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}
}