

# Dataset Card for "LexFiles"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Dataset Specifications](#supported-tasks-and-leaderboards)

## Dataset Description

- **Homepage:** https://github.com/coastalcph/lexlms
- **Repository:** https://github.com/coastalcph/lexlms
- **Paper:** https://arxiv.org/abs/xxx
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)

### Dataset Summary

**Disclaimer: This is a pre-proccessed version of the LexFiles corpus (https://huggingface.co/datasets/lexlms/lexfiles), where documents are pre-split in chunks of 512 tokens.**

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

| Corpus                            | Corpus alias         | Documents | Tokens | Pct.   | Sampl. (a=0.5) | Sampl. (a=0.2) |
|-----------------------------------|----------------------|-----------|--------|--------|----------------|----------------|
|  EU Legislation               | `eu-legislation`     | 93.7K     | 233.7M | 1.2%   | 5.0%           | 8.0%           |
| EU Court Decisions           | `eu-court-cases`     | 29.8K     | 178.5M | 0.9%   | 4.3%           | 7.6%           |
| ECtHR Decisions              | `ecthr-cases`        | 12.5K     | 78.5M  | 0.4%   | 2.9%           | 6.5%           |
| UK Legislation               | `uk-legislation`     | 52.5K     | 143.6M | 0.7%   | 3.9%           | 7.3%           |
| UK Court Decisions           | `uk-court-cases`     | 47K       | 368.4M | 1.9%   | 6.2%           | 8.8%           |
| Indian Court Decisions    | `indian-court-cases` | 34.8K     | 111.6M | 0.6%   | 3.4%           | 6.9%           |
| Canadian Legislation     | `canadian-legislation` | 6K        | 33.5M  | 0.2%   | 1.9%           | 5.5%           |
| Canadian Court Decisions | `canadian-court-cases` | 11.3K     | 33.1M  | 0.2%   | 1.8%           | 5.4%           |
| U.S. Court Decisions [1]     | `court-listener`     | 4.6M      | 11.4B  | 59.2%  | 34.7%          | 17.5%          |
| U.S. Legislation             | `us-legislation`     | 518       | 1.4B   | 7.4%   | 12.3%          | 11.5%          |
| U.S. Contracts               | `us-contracts`       | 622K      | 5.3B   | 27.3%  | 23.6%          | 15.0%          |
| Total                             | `lexlms/lexfiles`    | 5.8M      | 18.8B  | 100%   | 100%           | 100%           |

[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.

| Corpus                                 | Corpus alias           | Documents | Tokens | 
|----------------------------------------|------------------------|-----------|--------|
| Legal web pages from C4    | `legal-c4`             | 284K      | 340M   |


### 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.*](https://aclanthology.org/xxx/)
```
@inproceedings{chalkidis-garneau-etal-2023-lexlms,
    title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}},
    author = "Chalkidis*, Ilias and 
              Garneau*, Nicolas and
              Goanta, Catalina and 
              Katz, Daniel Martin and 
              Søgaard, Anders",
    booktitle = "Proceedings of the 61h Annual Meeting of the Association for Computational Linguistics",
    month = june,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/xxx",
}
```
task_categories:text-generation task_categories:fill-mask task_ids:language-modeling task_ids:masked-language-modeling annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:1M<n<10M source_datasets:extended language:en license:cc-by-nc-sa-4.0 region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "LexFiles", "configs": ["eu_legislation", "eu_court_cases", "uk_legislation", "uk_court_cases", "us_legislation", "us_court_cases", "us_contracts", "canadian_legislation", "canadian_court_cases", "indian_court_cases"]}
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended #language-English #license-cc-by-nc-sa-4.0 #region-us 

Dataset Card for "LexFiles"
===========================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Dataset Specifications


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Paper: URL
* Point of Contact: Ilias Chalkidis


### Dataset Summary


Disclaimer: This is a pre-proccessed version of the LexFiles corpus (URL where documents are pre-split in chunks of 512 tokens.


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.



*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.*



### Dataset Summary


Disclaimer: This is a pre-proccessed version of the LexFiles corpus (URL where documents are pre-split in chunks of 512 tokens.


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.



*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.*
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended #language-English #license-cc-by-nc-sa-4.0 #region-us 
 ### Dataset Summary


Disclaimer: This is a pre-proccessed version of the LexFiles corpus (URL where documents are pre-split in chunks of 512 tokens.


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.



*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.*
passage: TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended #language-English #license-cc-by-nc-sa-4.0 #region-us 
### Dataset Summary


Disclaimer: This is a pre-proccessed version of the LexFiles corpus (URL where documents are pre-split in chunks of 512 tokens.


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.