datasets:

• eduagarcia/LegalPT_dedup
• eduagarcia/CrawlPT_dedup language:
• pt pipeline_tag: fill-mask tags:
• legal model-index:
• name: RoBERTaLexPT-base results:
  • task: type: token-classification dataset: type: lener_br name: lener_br split: test metrics:
    • type: seqeval value: 0.9073 name: F1 args: scheme: IOB2
  • task: type: token-classification dataset: type: eduagarcia/PortuLex_benchmark name: UlyNER-PL Coarse config: UlyssesNER-Br-PL-coarse split: test metrics:
    • type: seqeval value: 0.8856 name: F1 args: scheme: IOB2
  • task: type: token-classification dataset: type: eduagarcia/PortuLex_benchmark name: UlyNER-PL Fine config: UlyssesNER-Br-PL-fine split: test metrics:
    • type: seqeval value: 0.8603 name: F1 args: scheme: IOB2
  • task: type: token-classification dataset: type: eduagarcia/PortuLex_benchmark name: FGV-STF config: fgv-coarse split: test metrics:
    • type: seqeval value: 0.8040 name: F1 args: scheme: IOB2
  • task: type: token-classification dataset: type: eduagarcia/PortuLex_benchmark name: RRIP config: rrip split: test metrics:
    • type: seqeval value: 0.8322 name: F1 args: scheme: IOB2
  • task: type: token-classification dataset: type: eduagarcia/PortuLex_benchmark name: PortuLex split: test metrics:
    • type: seqeval value: 0.8541 name: Average F1 args: scheme: IOB2 license: cc-by-4.0 metrics:
• seqeval

RoBERTaLexPT-base

RoBERTaLexPT-base is a Portuguese Masked Language Model pretrained from scratch from the LegalPT and CrawlPT corpora, using the same architecture as RoBERTa-base, introduced by Liu et al. (2019).

• Language(s) (NLP): Portuguese (pt-BR and pt-PT)
• License: Creative Commons Attribution 4.0 International Public License
• Repository: https://github.com/eduagarcia/roberta-legal-portuguese
• Paper: https://aclanthology.org/2024.propor-1.38/

Evaluation

The model was evaluated on "PortuLex" benchmark, a four-task benchmark designed to evaluate the quality and performance of language models in the Portuguese legal domain.

Macro F1-Score (%) for multiple models evaluated on PortuLex benchmark test splits:

In summary, RoBERTaLexPT consistently achieves top legal NLP effectiveness despite its base size. With sufficient pre-training data, it can surpass larger models. The results highlight the importance of domain-diverse training data over sheer model scale.

Training Details

RoBERTaLexPT-base is pretrained on:

• LegalPT is a Portuguese legal corpus by aggregating diverse sources of up to 125GiB data.
• CrawlPT is a composition of three Portuguese general corpora: brWaC, CC100 PT subset, OSCAR-2301 PT subset.

Training Procedure

Our pretraining process was executed using the Fairseq library v0.10.2 on a DGX-A100 cluster, utilizing a total of 2 Nvidia A100 80 GB GPUs. The complete training of a single configuration takes approximately three days.

This computational cost is similar to the work of BERTimbau-base, exposing the model to approximately 65 billion tokens during training.

Preprocessing

We deduplicated all subsets of the LegalPT and CrawlPT Corpus using the a MinHash algorithm and Locality Sensitive Hashing implementation from the libary text-dedup to find clusters of duplicate documents.

To ensure that domain models are not constrained by a generic vocabulary, we utilized the HuggingFace Tokenizers -- BPE algorithm to train a vocabulary for each pre-training corpus used.

Training Hyperparameters

The pretraining process involved training the model for 62,500 steps, with a batch size of 2048 and a learning rate of 4e-4, each sequence containing a maximum of 512 tokens.
The weight initialization is random.
We employed the masked language modeling objective, where 15% of the input tokens were randomly masked.
The optimization was performed using the AdamW optimizer with a linear warmup and a linear decay learning rate schedule.

For other parameters we adopted the standard RoBERTa-base hyperparameters:

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).