--- dataset_info: - config_name: acordaos_tcu 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: 2543994549.48221 num_examples: 462031 download_size: 1566036137 dataset_size: 2543994549.48221 - 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 num_bytes: 1555024472.2888384 num_examples: 310119 download_size: 853863429 dataset_size: 1555024472.2888384 - 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 num_bytes: 692805629.2689289 num_examples: 153373 download_size: 372281973 dataset_size: 692805629.2689289 - 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: - name: train num_bytes: 3523570990.7531776 num_examples: 542680 download_size: 1883985787 dataset_size: 3523570990.7531776 - 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 - name: is_duplicate dtype: bool - name: minhash_idx dtype: int64 splits: - name: train num_bytes: 28122511051.563988 num_examples: 6260096 download_size: 19944599978 dataset_size: 28122511051.563988 - 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 - name: cluster_size dtype: int64 - name: is_duplicate dtype: bool - name: minhash_idx dtype: int64 splits: - name: train num_bytes: 1134175020.033026 num_examples: 78893 download_size: 609610934 dataset_size: 1134175020.033026 - config_name: mlp_pt_eurlex-contracts 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: 343350961.1607806 num_examples: 8511 download_size: 99128584 dataset_size: 343350961.1607806 - 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: - name: train num_bytes: 2316503707.9080825 num_examples: 95024 download_size: 1051142246 dataset_size: 2316503707.9080825 - 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 num_bytes: 4400930935.870118 num_examples: 187637 download_size: 2206590934 dataset_size: 4400930935.870118 - 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: 2265120232.5456176 num_examples: 2109931 download_size: 1189159296 dataset_size: 2265120232.5456176 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-* --- # 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. ## 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 [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 ```bibtex @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).