--- datasets: - eduagarcia/LegalPT - eduagarcia/cc100-pt - eduagarcia/OSCAR-2301-pt_dedup - eduagarcia/brwac_dedup language: - pt pipeline_tag: fill-mask tags: - legal model-index: - name: RoBERTaLexPT-base results: - task: type: token-classification dataset: type: eduagarcia/portuguese_benchmark name: LeNER config: LeNER-Br split: test metrics: - type: seqeval value: 90.73 name: Mean F1 args: scheme: IOB2 - task: type: token-classification dataset: type: eduagarcia/portuguese_benchmark name: UlyNER-PL Coarse config: UlyssesNER-Br-PL-coarse split: test metrics: - type: seqeval value: 88.56 name: Mean F1 args: scheme: IOB2 - task: type: token-classification dataset: type: eduagarcia/portuguese_benchmark name: UlyNER-PL Fine config: UlyssesNER-Br-PL-fine split: test metrics: - type: seqeval value: 86.03 name: Mean F1 args: scheme: IOB2 license: cc-by-4.0 metrics: - seqeval --- # RoBERTaLexPT-base RoBERTaLexPT-base is pretrained from LegalPT and CrawlPT corpora, using [RoBERTa-base](https://huggingface.co/FacebookAI/roberta-base), introduced by [Liu et al. (2019)](https://arxiv.org/abs/1907.11692). - **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) - **Repository:** https://github.com/eduagarcia/roberta-legal-portuguese - **Paper:** [Coming soon] ## Evaluation The model was evaluated on ["PortuLex" benchmark](eduagarcia/portuguese_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](https://dl.acm.org/doi/abs/10.1007/978-3-030-61377-8_28) | 88.34 | 86.39/83.83 | 79.34 | 82.34 | 83.78 | | [BERTimbau-large](https://dl.acm.org/doi/abs/10.1007/978-3-030-61377-8_28) | 88.64 | 87.77/84.74 | 79.71 | **83.79** | 84.60 | | [Albertina-PT-BR-base](https://arxiv.org/abs/2305.06721) | 89.26 | 86.35/84.63 | 79.30 | 81.16 | 83.80 | | [Albertina-PT-BR-xlarge](https://arxiv.org/abs/2305.06721) | 90.09 | 88.36/**86.62** | 79.94 | 82.79 | 85.08 | | [BERTikal-base](https://arxiv.org/abs/2110.15709) | 83.68 | 79.21/75.70 | 77.73 | 81.11 | 79.99 | | [JurisBERT-base](https://repositorio.ufms.br/handle/123456789/5119) | 81.74 | 81.67/77.97 | 76.04 | 80.85 | 79.61 | | [BERTimbauLAW-base](https://repositorio.ufms.br/handle/123456789/5119) | 84.90 | 87.11/84.42 | 79.78 | 82.35 | 83.20 | | [Legal-XLM-R-base](https://arxiv.org/abs/2306.02069) | 87.48 | 83.49/83.16 | 79.79 | 82.35 | 83.24 | | [Legal-XLM-R-large](https://arxiv.org/abs/2306.02069) | 88.39 | 84.65/84.55 | 79.36 | 81.66 | 83.50 | | [Legal-RoBERTa-PT-large](https://arxiv.org/abs/2306.02069) | 87.96 | 88.32/84.83 | 79.57 | 81.98 | 84.02 | | RoBERTaTimbau-base | 89.68 | 87.53/85.74 | 78.82 | 82.03 | 84.29 | | RoBERTaLegalPT-base | 90.59 | 85.45/84.40 | 79.92 | 82.84 | 84.57 | | RoBERTaLexPT-base | **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 overparameterized models. The results highlight the importance of domain-diverse training data over sheer model scale. ## Training Details RoBERTaLexPT-base is pretrained from both data: - [LegalPT](https://huggingface.co/datasets/eduagarcia/LegalPT) is a Portuguese legal corpus by aggregating diverse sources of up to 125GiB data. - CrawlPT is a duplication of three Portuguese general corpora: [brWaC](https://huggingface.co/datasets/eduagarcia/brwac_dedup), [CC100-PT](https://huggingface.co/datasets/eduagarcia/cc100-pt), [OSCAR-2301](https://huggingface.co/datasets/eduagarcia/OSCAR-2301-pt_dedup). ### Training Procedure Our pretraining process was executed using the [Fairseq library](https://arxiv.org/abs/1904.01038) 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 setup is similar to the work of [BERTimbau](https://dl.acm.org/doi/abs/10.1007/978-3-030-61377-8_28), exposing the model to approximately 65 billion tokens during training. #### Preprocessing Following the approach of [Lee et al. (2022)](http://arxiv.org/abs/2107.06499), we deduplicated all subsets of the LegalPT Corpus using the [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) to find clusters of duplicate documents. To ensure that domain models are not constrained by a generic vocabulary, we utilized the [HuggingFace Tokenizers](https://github.com/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 sequences, each containing a maximum of 512 tokens. 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 hyperparameters we adopted the standard [RoBERTa hyperparameters](https://arxiv.org/abs/1907.11692): | **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{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).