---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine-tuned-bertimbau-for-legal-area-classification-v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Fine-tuned BERTImbau for legal texts classification

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/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