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---
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).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Funded by:** [More Information Needed]
- **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)
### Model Sources
- **Repository:** https://github.com/eduagarcia/roberta-legal-portuguese
- **Paper:** [More Information Needed]
## Training Details
### Training Data
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.
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 |
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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.
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Citation
[More Information Needed]
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