Dataset Card for "LexFiles"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Dataset Specifications

Dataset Description

• Homepage: https://github.com/coastalcph/lexlms
• Repository: https://github.com/coastalcph/lexlms
• Paper: https://arxiv.org/abs/2305.07507
• Point of Contact: Ilias Chalkidis

Dataset Summary

The LeXFiles is a new diverse English multinational legal corpus that we created including 11 distinct sub-corpora that cover legislation and case law from 6 primarily English-speaking legal systems (EU, CoE, Canada, US, UK, India). The corpus contains approx. 19 billion tokens. In comparison, the "Pile of Law" corpus released by Hendersons et al. (2022) comprises 32 billion in total, where the majority (26/30) of sub-corpora come from the United States of America (USA), hence the corpus as a whole is biased towards the US legal system in general, and the federal or state jurisdiction in particular, to a significant extent.

Dataset Specifications

[1] We consider only U.S. Court Decisions from 1965 onwards (cf. post Civil Rights Act), as a hard threshold for cases relying on severely out-dated and in many cases harmful law standards. The rest of the corpora include more recent documents.

[2] Sampling (Sampl.) ratios are computed following the exponential sampling introduced by Lample et al. (2019).

Additional corpora not considered for pre-training, since they do not represent factual legal knowledge.

Usage

Load a specific sub-corpus, given the corpus alias, as presented above.

from datasets import load_dataset

dataset = load_dataset('lexlms/lex_files', name='us-court-cases')

Citation

Ilias Chalkidis*, Nicolas Garneau*, Catalina E.C. Goanta, Daniel Martin Katz, and Anders Søgaard. LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development. 2022. In the Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics. Toronto, Canada.

@inproceedings{chalkidis-etal-2023-lexfiles,
    title = "{L}e{XF}iles and {L}egal{LAMA}: Facilitating {E}nglish Multinational Legal Language Model Development",
    author = "Chalkidis, Ilias  and
      Garneau, Nicolas  and
      Goanta, Catalina  and
      Katz, Daniel  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.865",
    pages = "15513--15535",
}