caselaw_access_project

# Caselaw Access Project

## Description
This dataset contains 6.7 million cases from the Caselaw Access Project and Court Listener.
The Caselaw Access Project consists of nearly 40 million pages of U.S. federal and state court decisions and judges’ opinions from the last 365 years.
In addition, Court Listener adds over 900 thousand cases scraped from 479 courts.
The Caselaw Access Project and Court Listener source legal data from a wide variety of resources such as the Harvard Law Library, the Law Library of Congress, and the Supreme Court Database.
From these sources, we only included documents that were in the public domain.
Erroneous OCR errors were further corrected after digitization, and additional post-processing was done to fix formatting and parsing.
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 |
|-----------|----------|
| 6,919,240 | 78 |

## 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 "raw" version of the Caselaw Access Project dataset. If you are looking for the filtered version used to train [Comma v0.1](https://huggingface.co/common-pile/comma-v0.1), you can find it [here](https://huggingface.co/datasets/common-pile/caselaw_access_project_filtered).

## 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}
}
```

# 案例法访问项目（Caselaw Access Project）

## 数据集说明
本数据集包含来自案例法访问项目（Caselaw Access Project）与Court Listener的670万份案例（精确统计见下文数据集统计项）。案例法访问项目收录了过去365年间美国联邦及州法院的判决文书与法官意见书，总计近4000万页内容。此外，Court Listener补充了从479个法院抓取的超90万份案例。
案例法访问项目与Court Listener的法律数据来源于多类权威渠道，包括哈佛大学法学院图书馆、美国国会图书馆法律部以及最高法院数据库等。本次数据集仅收录来自上述渠道且处于公共领域的文档。
数字化流程完成后，团队会进一步修正OCR（光学字符识别）识别误差，并通过额外后处理工序修复格式与解析问题。本数据集的采集、处理与制备代码可于[common-pile GitHub仓库](https://github.com/r-three/common-pile)获取。

## 数据集统计
| 文档数量 | UTF-8 存储容量（GB） |
|---------|---------------------|
| 6,919,240 | 78 |

## 许可问题
尽管我们致力于打造许可信息完全准确的数据集，但许可洗白与元数据不准确的问题可能导致我们为部分文档错误分配了不当许可（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许可信息错误的情况，请前往本仓库[发起讨论](https://github.com/r-three/common-pile/discussions/new)。

## 其他版本
本版本为案例法访问项目数据集的「原始版」。若您需查找用于训练[Comma v0.1](https://huggingface.co/common-pile/comma-v0.1)的过滤版数据集，可前往[此处](https://huggingface.co/datasets/common-pile/caselaw_access_project_filtered)获取。

## 引用说明
若您使用本数据集，请引用以下文献：
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}
}