Roberta Legal Portuguese ⚖️
Collection
Related resources to the paper RoBERTaLexPT: A Legal RoBERTa Model pretrained with deduplication for Portuguese
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Datasets:
Access PortuLex on Hugging Face
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The PortuLex benchmark includes datasets with specific access requirements:
- RRI dataset requires the acceptance of these terms: https://bit.ly/rhetoricalrole.
- For the FGV-STF corpus, you must request it directly from the original authors: https://www.sciencedirect.com/science/article/abs/pii/S0306457321002727.
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PortuLex_benchmark
"PortuLex" benchmark is a four-task benchmark designed to evaluate the quality and performance of language models in the Portuguese legal domain.
| Dataset | Task | Train | Dev | Test |
|---|---|---|---|---|
| RRI | CLS | 8.26k | 1.05k | 1.47k |
| LeNER-Br | NER | 7.83k | 1.18k | 1,39k |
| UlyssesNER-Br | NER | 3.28k | 489 | 524 |
| FGV-STF | NER | 415 | 60 | 119 |
Dataset Details
PortuLex is composed by: LeNER-Br, Rhetorical Role Identification (RRI), FGV-STF, UlyssesNER-Br.
- LeNER-Br: the first Named Entity Recognition (NER) corpus for the legal domain in Brazilian Portuguese from higher and state-level courts.
- RRI: rhetorical annotations from judicial sentences from the Court of Justice of Mato Grosso do Sul (Brazil).
- FGV-STF: decisions from the Supreme Federal Court for entity extraction.
- UlyssesNER-Br: NER corpus of bills and legislative queries from the Chamber of Deputies of Brazil.
Dataset Evaluation
Macro F1-Score (%) for multiple models evaluated on PortuLex benchmark test splits:
| Model | LeNER | UlyNER-PL | FGV-STF | RRIP | Average (%) |
|---|---|---|---|---|---|
| Coarse/Fine | Coarse | ||||
| BERTimbau-based | 88.34 | 86.39/83.83 | 79.34 | 82.34 | 83.78 |
| BERTimbau-large | 88.64 | 87.77/84.74 | 79.71 | 83.79 | 84.60 |
| Albertina-PT-BR-base | 89.26 | 86.35/84.63 | 79.30 | 81.16 | 83.80 |
| Albertina-PT-BR-xlarge | 90.09 | 88.36/86.62 | 79.94 | 82.79 | 85.08 |
| BERTikal-base | 83.68 | 79.21/75.70 | 77.73 | 81.11 | 79.99 |
| JurisBERT-base | 81.74 | 81.67/77.97 | 76.04 | 80.85 | 79.61 |
| BERTimbauLAW-base | 84.90 | 87.11/84.42 | 79.78 | 82.35 | 83.20 |
| Legal-XLM-R-base | 87.48 | 83.49/83.16 | 79.79 | 82.35 | 83.24 |
| Legal-XLM-R-large | 88.39 | 84.65/84.55 | 79.36 | 81.66 | 83.50 |
| Legal-RoBERTa-PT-large | 87.96 | 88.32/84.83 | 79.57 | 81.98 | 84.02 |
| Ours | |||||
| RoBERTaTimbau-base (Reproduction of BERTimbau) | 89.68 | 87.53/85.74 | 78.82 | 82.03 | 84.29 |
| RoBERTaLegalPT-base (Trained on LegalPT) | 90.59 | 85.45/84.40 | 79.92 | 82.84 | 84.57 |
| RoBERTaCrawlPT-base (Trained on CrawlPT) | 89.24 | 88.22/86.58 | 79.88 | 82.80 | 84.83 |
| RoBERTaLexPT-base (Trained on CrawlPT + LegalPT) | 90.73 | 88.56/86.03 | 80.40 | 83.22 | 85.41 |
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).
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Language(s) (NLP):
Brazilian Portuguese (pt-BR)
License:
Creative Commons Attribution 4.0 International Public License
Repository:
github.com