

# 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/2305.07507
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)

### 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

| 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]     | `us-court-cases`     | 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/lex_files`    | 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   |


### Usage

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

```python
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.*](https://aclanthology.org/2023.acl-long.865/)
```
@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",
}

```
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 legal law arxiv:2305.07507 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", "tags": ["legal", "law"]}
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 #legal #law #arxiv-2305.07507 #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


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.


*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


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.


*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 #legal #law #arxiv-2305.07507 #region-us 
 ### 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.


*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.*