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Datasets:
eduagarcia
/
PortuLex_benchmark

Tasks:
Token Classification
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Portuguese
Size:
10K - 100K
Tags:
legal
Libraries:
Datasets
pandas
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The PortuLex benchmark includes datasets with specific access requirements:

  1. RRI dataset requires the acceptance of these terms: https://bit.ly/rhetoricalrole.
  2. 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

Collection including eduagarcia/PortuLex_benchmark