metadata

license: cc0-1.0
task_categories:
  - text-generation
language:
  - en
tags:
  - legal
  - law
  - caselaw
pretty_name: Caselaw Access Project
size_categories:
  - 1M<n<10M

The Caselaw Access Project

In collaboration with Ravel Law, Harvard Law Library digitized over 40 million U.S. court decisions consisting of 6.7 million cases from the last 360 years into a dataset that is widely accessible to use. Access a bulk download of the data through the Caselaw Access Project API (CAPAPI): https://case.law/caselaw/

Find more information about accessing state and federal written court decisions of common law through the bulk data service documentation here: https://case.law/docs/

Learn more about the Caselaw Access Project and all of the phenomenal work done by Jack Cushman, Greg Leppert, and Matteo Cargnelutti here: https://case.law/about/

Watch a live stream of the data release here: https://lil.law.harvard.edu/about/cap-celebration/stream

Post-processing

Teraflop AI is excited to help support the Caselaw Access Project and Harvard Library Innovation Lab, in the release of over 6.6 million state and federal court decisions published throughout U.S. history. It is important to democratize fair access to data to the public, legal community, and researchers. This is a processed and cleaned version of the original CAP data.

During the digitization of these texts, there were erroneous OCR errors that occurred. We worked to post-process each of the texts for model training to fix encoding, normalization, repetition, redundancy, parsing, and formatting.

Teraflop AI’s data engine allows for the massively parallel processing of web-scale datasets into cleaned text form. Our one-click deployment allowed us to easily split the computation between 1000s of nodes on our managed infrastructure.

FAISS Index

We built a FAISS index over all of the post-processed legal texts. The index consists of ~6.6 million dense vectors and the average search speed of a query over the entire index is 12.46 milliseconds.

The FAISS library by @Meta allows you to perform k-nearest neighbor search efficiently and in a scalable way over millions of dense vectors. Find the FAISS library here: https://github.com/facebookresearch/faiss

The combination of an Inverted File Index (IVF), Product quantization (PQ), and Hierarchical Navigable Small World (HNSW) allows us to run these queries across all of the dense vectors in milliseconds. Find more information about everything here: https://github.com/facebookresearch/faiss/wiki/Faiss-indexes

Licensing Information

The Caselaw Access Project dataset is licensed under the CC0 License.

Citation Information

The President and Fellows of Harvard University. "Caselaw Access Project." 2024, https://case.law/

@misc{ccap,
    title={Cleaned Caselaw Access Project},
    author={Enrico Shippole, Aran Komatsuzaki},
    howpublished{\url{https://huggingface.co/datasets/TeraflopAI/Caselaw_Access_Project}},
    year={2024}
}