README.md

metadata

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - monolingual
pretty_name: pile-of-law
size_categories:
  - 10M<n<100M
source_datasets: []
task_categories:
  - fill-mask
task_ids:
  - masked-language-modeling
viewer: false

Dataset Card for Pile of Law

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://huggingface.co/datasets/pile-of-law/pile-of-law
• Repository: https://huggingface.co/datasets/pile-of-law/pile-of-law
• Paper: https://arxiv.org/abs/2207.00220

Dataset Summary

We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives.

Supported Tasks and Leaderboards

See paper for details.

Languages

English

Dataset Structure

Data Instances

courtListener_docket_entry_documents : Docket entries in U.S. federal courts, including filed briefs from CourtListener RECAP archive.

courtListener_opinions : U.S. court opinions from CourtListener.

atticus_contracts: Unannotated contracts from the Atticus Project.

federal_register: The U.S. federal register where agencies file draft rulemaking.

bva_opinions: Bureau of Veterans Appeals opinions.

us_bills: Draft Bills from the United States Congress.

cc_casebooks: Educational Casebooks released under open CC licenses.

tos: Unannotated Terms of Service contracts.

euro_parl: European parliamentary debates.

nlrb_decisions: Decisions from the U.S. National Labor Review Board.

scotus_oral_arguments: U.S. Supreme Court Oral Arguments

cfr: U.S. Code of Federal Regulations

state_codes: U.S. State Codes

scotus_filings: Briefs and filings with the U.S. Supreme Court.

bar_exam_outlines: Bar exam outlines available openly on the web.

edgar: Contracts filed with the SEC and made available on the SEC's Edgar tool.

cfpb_creditcard_contracts: Credit Card Contracts compiled by the U.S. Consumer Finance Protection Bureau.

constitutions : The World's constitutions.

congressional_hearings : U.S. Congressional hearing transcripts and statements.

oig: U.S. Office of Inspector general reports.

olc_memos: U.S. Office of Legal Counsel memos.

uscode: The United States Code (laws).

founding_docs: Letters from U.S. founders.

ftc_advisory_opinions: Advisory opinions by the Federal Trade Commission.

echr : European Court of Human Rights opinions.

eurlex: European Laws.

tax_rulings: Rulings from U.S. Tax court.

un_debates: U.N. General Debates

fre: U.S. Federal Rules of Evidence

frcp : U.S. Federal Rules of Civil Procedure

canadian_decisions: Canadian Court Opinions from ON and BC.

eoir: U.S. Executive Office for Immigration Review Immigration and Nationality Precedential Decisions

dol_ecab: Department of Labor Employees' Compensation Appeals Board decisions after 2006

r_legaladvice : Filtered data from the r/legaladvice and r/legaladviceofftopic subreddits in the format. Title: [Post Title] Question: [Post Content] Topic: [Post Flair] Answer #[N]: [Top Answers]...

Data Fields

• text: the document text
• created_timestamp: If the original source provided a timestamp when the document was created we provide this as well. Note, these may be inaccurate. For example CourtListener case opinions provide the timestamp of when it was uploaded to CourtListener not when the opinion was published. We welcome pull requests to correct this field if such inaccuracies are discovered.
• downloaded_timestamp: When the document was scraped.
• url: the source url

Data Splits

There is a train/validation split for each subset of the data. 75%/25%

Dataset Creation

Curation Rationale

We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives. As such, data sources are curated to inform: (1) legal analysis, knowledge, or understanding; (2) argument formation; (3) privacy filtering standards. Sources like codes and laws tend to inform (1). Transcripts and court filings tend to inform (2). Opinions tend to inform (1) and (3).

Source Data

Initial Data Collection and Normalization

We do not normalize the data, but we provide dataset creation code and relevant urls in https://github.com/Breakend/PileOfLaw

Who are the source language producers?

Varied (see sources above).

Personal and Sensitive Information

This dataset may contain personal and sensitive information. However, this has been previously filtered by the relevant government and federal agencies that weigh the harms of revealing this information against the benefits of transparency. If you encounter something particularly harmful, please file a takedown request with the upstream source and notify us in the communities tab. We will then remove the content. We cannot enable more restrictive licensing because upstream sources may restrict using a more restrictive license. However, we ask that all users of this data respect the upstream licenses and restrictions. Per the standards of CourtListener, we do not allow indexing of this data by search engines and we ask that others do not also. Please do not turn on anything that allows the data to be easily indexed.

Considerations for Using the Data

Social Impact of Dataset

We hope that this dataset will provide more mechanisms for doing data work. As we describe in the paper, the internal variation allows contextual privacy rules to be learned. If robust mechanisms for this are developed they can applied more broadly. This dataset can also potentially be used for legal language model pretraining. As discussed in ``On the Opportunities and Risks of Foundation Models'', legal language models can help improve access to justice in various ways. But they can also be used in potentially harmful ways. While such models are not ready for most production environments and are the subject of significant research, we ask that model creators using this data, particularly when creating generative models, consider the impacts of their model and make a good faith effort to weigh the benefits against the harms of their method. Our license and many of the sub-licenses also restrict commercial usage.

Discussion of Biases

The data reflects the biases of governments and courts. As we discuss in our work, these can be significant, though more recent text will likely be less overtly toxic. Please see the above statement and embark on any model uses responsibly.

Other Known Limitations

We mainly focus on U.S. and English-speaking legal sources, though we include some European and Canadian resources.

Additional Information

Licensing Information

CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International. But individual sources may have other licenses. See paper for details. Some upstream data sources request that indexing be disabled. As such please do not re-host any data in a way that can be indexed by search engines.

No Representations

We do not make any representation that the legal information provided here is accurate. It is meant for research purposes only. For the authoritative and updated source of information please refer directly to the governing body which provides the latest laws, rules, and regulations relevant to you.

DMCA Takedown Requests

Pile of Law follows the notice and takedown procedures in the Digital Millennium Copyright Act (DMCA), 17 U.S.C. Section 512.

If you believe content on Pile of Law violates your copyright, please immediately notify its operators by sending a message with the information described below. Please use the subject "Copyright" in your message. If Pile of Law's operators act in response to an infringement notice, they will make a good-faith attempt to contact the person who contributed the content using the most recent email address that person provided to Pile of Law.

Under the DMCA, you may be held liable for damages based on material misrepresentations in your infringement notice. You must also make a good-faith evaluation of whether the use of your content is a fair use, because fair uses are not infringing. See 17 U.S.C. Section 107 and Lenz v. Universal Music Corp., No. 13-16106 (9th Cir. Sep. 14, 2015). If you are not sure if the content you want to report infringes your copyright, you should first contact a lawyer.

The DMCA requires that all infringement notices must include all of the following:

• A signature of the copyright owner or a person authorized to act on the copyright owner's behalf
• An identification of the copyright claimed to have been infringed
• A description of the nature and location of the material that you claim to infringe your copyright, in sufficient detail to allow Pile of Law to find and positively identify that material
• Your name, address, telephone number, and email address
• A statement that you believe in good faith that the use of the material that you claim to infringe your copyright is not authorized by law, or by the copyright owner or such owner's agent
• A statement, under penalty of perjury, that all of the information contained in your infringement notice is accurate
• A statement, under penalty of perjury, that you are either the copyright owner or a person authorized to act on their behalf.

Pile of Law will respond to all DMCA-compliant infringement notices, including, as required or appropriate, by removing the offending material or disabling all links to it.

All received infringement notices may be posted in full to the Lumen database (previously known as the Chilling Effects Clearinghouse).

All takedown requests with the above information should be sent via email to pileoflaw@gmail.com.

This removal notice has been modified from the (CourtListener DMCA takedown notice)[https://www.courtlistener.com/terms/].

Citation Information

For a citation to this work:

@misc{hendersonkrass2022pileoflaw,
  url = {https://arxiv.org/abs/2207.00220},
  author = {Henderson*, Peter and Krass*, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
  title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
  publisher = {arXiv},
  year = {2022}
}

Since this dataset also includes several other data sources with citations, please refer to our paper and cite the additional relevant work in addition to our own work.