Datasets:
metadata
language:
- pt
license: cc-by-4.0
size_categories:
- 10M<n<100M
pretty_name: LegalPT (deduplicated)
dataset_info:
- config_name: acordaos_tcu
features:
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- name: text
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- name: meta
struct:
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struct:
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struct:
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- config_name: mlp_pt_eurlex-legislation
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struct:
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struct:
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struct:
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splits:
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- config_name: parlamento-pt
features:
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- name: text
dtype: string
- name: meta
struct:
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struct:
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struct:
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dtype: int64
- name: cluster_size
dtype: int64
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dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
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dtype: int64
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splits:
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- config_name: tesemo_v2
features:
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dtype: int64
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dtype: string
- name: meta
struct:
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struct:
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struct:
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dtype: int64
- name: cluster_size
dtype: int64
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dtype: int64
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dtype: bool
- name: minhash
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dtype: int64
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dtype: int64
splits:
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configs:
- config_name: acordaos_tcu
data_files:
- split: train
path: acordaos_tcu/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-*
- config_name: mlp_pt_legal-mc4
data_files:
- split: train
path: mlp_pt_legal-mc4/train-*
- config_name: parlamento-pt
data_files:
- split: train
path: parlamento-pt/train-*
- config_name: tesemo_v2
data_files:
- split: train
path: tesemo_v2/train-*
tags:
- legal
LegalPT (deduplicated)
LegalPT aggregates the maximum amount of publicly available legal data in Portuguese, drawing from varied sources including legislation, jurisprudence, legal articles, and government documents.
This version is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022). The raw version is also available here.
Dataset Details
Dataset is composed by six corpora: Ulysses-Tesemõ, MultiLegalPile (PT), ParlamentoPT, Iudicium Textum, Acordãos TCU, and DataSTF.
- 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), the Portuguese subset of legal documents from the European Union, known as EUR-Lex, and a filter for legal documents from MC4.
- 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
- Language(s) (NLP): Brazilian Portuguese (pt-BR)
- License: Creative Commons Attribution 4.0 International Public License
- Repository: https://github.com/eduagarcia/roberta-legal-portuguese
- Paper: [More Information Needed]
Data Collection and Processing
LegalPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).
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
@InProceedings{garcia2024_roberlexpt,
author="Garcia, Eduardo A. S.
and Silva, N{\'a}dia F. F.
and Siqueira, Felipe
and Gomes, Juliana R. S.
and Albuqueruqe, Hidelberg O.
and Souza, Ellen
and Lima, Eliomar
and De Carvalho, André",
title="RoBERTaLexPT: A Legal RoBERTa Model pretrained with deduplication for Portuguese",
booktitle="Computational Processing of the Portuguese Language",
year="2024",
publisher="Association for Computational Linguistics"
}
Acknowledgment
This work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG).