---
language:
- pt
license: cc-by-4.0
size_categories:
- 1B<n<10B
task_categories:
- text-generation
tags:
- legal
dataset_info:
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- config_name: iudicium_textum
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- config_name: mlp_pt_eurlex-contracts
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    struct:
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- config_name: mlp_pt_eurlex-legislation
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configs:
- config_name: all
  data_files:
  - split: train
    path: all/train-*
- config_name: datastf
  data_files:
  - split: train
    path: datastf/train-*
- config_name: iudicium_textum
  data_files:
  - split: train
    path: iudicium_textum/train-*
- config_name: mlp_pt_BRCAD-5
  data_files:
  - split: train
    path: mlp_pt_BRCAD-5/train-*
- config_name: mlp_pt_CJPG
  data_files:
  - split: train
    path: mlp_pt_CJPG/train-*
- config_name: mlp_pt_eurlex-caselaw
  data_files:
  - split: train
    path: mlp_pt_eurlex-caselaw/train-*
- config_name: mlp_pt_eurlex-contracts
  data_files:
  - split: train
    path: mlp_pt_eurlex-contracts/train-*
- config_name: mlp_pt_eurlex-legislation
  data_files:
  - split: train
    path: mlp_pt_eurlex-legislation/train-*
---
# LegalPT

LegalPT aggregates the maximum amount of publicly available legal data in Portuguese, drawing from varied sources including legislation, jurisprudence, legal articles, and government documents.

## Dataset Details

Dataset is composed by six corpora: 
[Ulysses-Tesemõ](https:github.com/ulysses-camara/ulysses-tesemo), [MultiLegalPile (PT)](https://arxiv.org/abs/2306.02069v2), [ParlamentoPT](http://arxiv.org/abs/2305.06721),
[Iudicium Textum](https://www.inf.ufpr.br/didonet/articles/2019_dsw_Iudicium_Textum_Dataset.pdf), [Acordãos TCU](https://link.springer.com/chapter/10.1007/978-3-030-61377-8_46), and 
[DataSTF](https://legalhackersnatal.wordpress.com/2019/05/09/mais-dados-juridicos/).

- **MultiLegalPile**: a multilingual corpus of legal texts comprising 689 GiB of data, covering 24 languages in 17 jurisdictions. The corpus is separated by language, and the subset in Portuguese contains 92GiB of data, containing 13.76 billion words. This subset includes the jurisprudence of the Court of Justice of São Paulo (CJPG), appeals from the [5th Regional Federal Court (BRCAD-5)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272287), the Portuguese subset of legal documents from the European Union, known as [EUR-Lex](https://eur-lex.europa.eu/homepage.html), and a filter for legal documents from [MC4](http://arxiv.org/abs/2010.11934).
- **Ulysses-Tesemõ**: a legal corpus in Brazilian Portuguese, composed of 2.2 million documents, totaling about 26GiB of text obtained from 96 different data sources. These sources encompass legal, legislative, academic papers, news, and related comments. The data was collected through web scraping of government websites.
- **ParlamentoPT**: a corpus for training language models in European Portuguese. The data was collected from the Portuguese government portal and consists of 2.6 million documents of transcriptions of debates in the Portuguese Parliament.
- **Iudicium Textum**: consists of rulings, votes, and reports from the Supreme Federal Court (STF) of Brazil, published between 2010 and 2018. The dataset contains 1GiB of data extracted from PDFs.
- **Acordãos TCU**:  an open dataset from the Tribunal de Contas da União (Brazilian Federal Court of Accounts), containing 600,000 documents obtained by web scraping government websites. The documents span from 1992 to 2019.
- **DataSTF**: a dataset of monocratic decisions from the Superior Court of Justice (STJ) in Brazil, containing 700,000 documents (5GiB of data).

### Dataset Description

- **Curated by:** [More Information Needed]
- **Funded by:** [More Information Needed]
- **Language(s) (NLP):** Brazilian Portuguese (pt-BR)
- **License:** [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/deed.en)

### Dataset Sources

- **Repository:** https://github.com/eduagarcia/roberta-legal-portuguese
- **Paper:** [More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Data Collection and Processing

LegalPT is deduplicated using [MinHash algorithm](https://dl.acm.org/doi/abs/10.5555/647819.736184) and [Locality Sensitive Hashing](https://dspace.mit.edu/bitstream/handle/1721.1/134231/v008a014.pdf?sequence=2&isAllowed=y), following the approach of [Lee et al. (2022)](http://arxiv.org/abs/2107.06499).

We used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.

Duplicate rate found by the Minhash-LSH algorithm  for the LegalPT corpus:

| **Corpus**               |  **Documents** | **Docs. after deduplication** | **Duplicates (%)** |
|--------------------------|:--------------:|:-----------------------------:|:------------------:|
| Ulysses-Tesemõ           |    2,216,656   |           1,737,720           |        21.61       |
| MultiLegalPile (PT)      |                |                               |                    |
|    CJPG                  |   14,068,634   |           6,260,096           |        55.50       |
|    BRCAD-5               |    3,128,292   |            542,680            |        82.65       |
|    EUR-Lex (Caselaw)     |     104,312    |             78,893            |        24.37       |
|    EUR-Lex (Contracts)   |     11,581     |             8,511             |        26.51       |
|    EUR-Lex (Legislation) |     232,556    |             95,024            |        59.14       |
|    Legal MC4             |     191,174    |            187,637            |        1.85        |
| ParlamentoPT             |    2,670,846   |           2,109,931           |        21.00       |
| Iudicium Textum          |     198,387    |            153,373            |        22.69       |
| Acordãos TCU             |     634,711    |            462,031            |        27.21       |
| DataSTF                  |     737,769    |            310,119            |        57.97       |
| **Total (LegalPT)**      | **24,194,918** |         **11,946,015**        |      **50.63**     |

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->