Datasets:
metadata
language:
- pt
size_categories:
- 10M<n<100M
task_categories:
- text-generation
tags:
- legal
dataset_info:
- config_name: all
features:
- name: id
dtype: int64
- name: source
dtype: string
- name: orig_id
dtype: int64
- name: text
dtype: string
splits:
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dataset_size: 135151899572
- config_name: acordaos_tcu
features:
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- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
splits:
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- config_name: datastf
features:
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
- name: id
dtype: int64
splits:
- name: train
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num_examples: 737769
download_size: 1724245648
dataset_size: 3699382656
- config_name: iudicium_textum
features:
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
- name: id
dtype: int64
splits:
- name: train
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download_size: 408025309
dataset_size: 896139675
- config_name: mlp_pt_BRCAD-5
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
splits:
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download_size: 9735599974
dataset_size: 20311710293
- config_name: mlp_pt_CJPG
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
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dtype: bool
- name: minhash_idx
dtype: int64
splits:
- name: train
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dataset_size: 63201157801
- config_name: mlp_pt_eurlex-caselaw
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
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dtype: int64
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dtype: bool
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dtype: int64
splits:
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dataset_size: 1499601545
- config_name: mlp_pt_eurlex-contracts
features:
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dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
splits:
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download_size: 112805426
dataset_size: 467200973
- config_name: mlp_pt_eurlex-legislation
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
splits:
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download_size: 1384571339
dataset_size: 5669271303
- config_name: mlp_pt_legal-mc4
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
splits:
- name: train
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num_examples: 191174
download_size: 2250422592
dataset_size: 4483889482
- config_name: parlamento-pt
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
splits:
- name: train
num_bytes: 2867291543
num_examples: 2670846
download_size: 1319479156
dataset_size: 2867291543
- config_name: tesemo_v2
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: dedup
struct:
- name: exact_norm
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: exact_hash_idx
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash
struct:
- name: cluster_main_idx
dtype: int64
- name: cluster_size
dtype: int64
- name: is_duplicate
dtype: bool
- name: minhash_idx
dtype: int64
splits:
- name: train
num_bytes: 29158221995
num_examples: 2216656
download_size: 13543440397
dataset_size: 29158221995
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
- 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-*
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.
This is the raw version. Deduplicated version is available here.
Dataset Details
Dataset is composed by six corpora: Ulysses-Tesemõ, MultiLegalPile (PT), ParlamentoPT, Iudicium Textum, Acordãos TCU, and DataSTF.
- MultiLegalPile (Paper): 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 (Paper): 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 (Paper): 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 (Paper): 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): Portuguese (pt-BR and pt-PT)
- Repository: https://github.com/eduagarcia/roberta-legal-portuguese
- Paper: https://aclanthology.org/2024.propor-1.38/
Citation
@inproceedings{garcia-etal-2024-robertalexpt,
title = "{R}o{BERT}a{L}ex{PT}: A Legal {R}o{BERT}a Model pretrained with deduplication for {P}ortuguese",
author = "Garcia, Eduardo A. S. and
Silva, Nadia F. F. and
Siqueira, Felipe and
Albuquerque, Hidelberg O. and
Gomes, Juliana R. S. and
Souza, Ellen and
Lima, Eliomar A.",
editor = "Gamallo, Pablo and
Claro, Daniela and
Teixeira, Ant{\'o}nio and
Real, Livy and
Garcia, Marcos and
Oliveira, Hugo Gon{\c{c}}alo and
Amaro, Raquel",
booktitle = "Proceedings of the 16th International Conference on Computational Processing of Portuguese",
month = mar,
year = "2024",
address = "Santiago de Compostela, Galicia/Spain",
publisher = "Association for Computational Lingustics",
url = "https://aclanthology.org/2024.propor-1.38",
pages = "374--383",
}
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).