metadata

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: 0.804
            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): Brazilian Portuguese (pt-BR)
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:

Model	LeNER	UlyNER-PL	FGV-STF	RRIP	Average (%)
		Coarse/Fine	Coarse
BERTimbau-base	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 (this) (Trained on CrawlPT + LegalPT)	90.73	88.56/86.03	80.40	83.22	85.41

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:

Hyperparameter	RoBERTa-base
Number of layers	12
Hidden size	768
FFN inner hidden size	3072
Attention heads	12
Attention head size	64
Dropout	0.1
Attention dropout	0.1
Warmup steps	6k
Peak learning rate	4e-4
Batch size	2048
Weight decay	0.01
Maximum training steps	62.5k
Learning rate decay	Linear
AdamW $$\epsilon$$	1e-6
AdamW $$\beta_1$$	0.9
AdamW $$\beta_2$$	0.98
Gradient clipping	0.0

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).