This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on a dataset containing summaries of TJSP decisions, with the purpose of classyfing the text on 5 legal areas. It achieves the following results on the evaluation set:

Loss: 0.5813
Accuracy: 0.8713

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2709	1.0	8509	0.5307	0.8388
0.2388	2.0	17018	0.4947	0.8692
0.1761	3.0	25527	0.5813	0.8713

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu118
Datasets 2.20.0
Tokenizers 0.19.1

lucasbalponti/fine-tuned-bertimbau-for-legal-area-classification-v1

Fine-tuned BERTImbau for legal texts classification

Training procedure

Training hyperparameters

Training results

Framework versions

lucasbalponti/
fine-tuned-bertimbau-for-legal-area-classification-v1