---
datasets:
- legal_documents
language: pt
license: apache-2.0
tags:
- text-classification
- legal
- bert
- portuguese
- brazilian-legal-documents
model-index:
- name: testmodel
  results:
  - task:
      type: text-classification
    dataset:
      name: Legal Documents Classification Dataset
      type: legal_documents
    metrics:
    - type: accuracy
      value: 0.9352226720647774
    - type: f1
      value: 0.9328717264101649
    - type: precision
      value: 0.9342469820430453
    - type: recall
      value: 0.9352226720647774
    - type: f1_macro
      value: 0.8259694427208276
---

# Model Card for Model ID

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## Model Details

### Model Description

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# Modelo de Classificação de Documentos Jurídicos

Este modelo foi treinado para classificar documentos jurídicos brasileiros usando BERT multilíngue.

## Detalhes do Modelo

- **Base Model:** google-bert/bert-base-multilingual-cased
- **Tipo:** Classificação Multiclasse
- **Número de Classes:** 7
- **Idioma:** Português (Brasil)
- **Domínio:** Documentos Jurídicos

## Classes

0. Alvará
1. Sentença
2. Penhora/Arresto/Sequestro
3. Citação
4. Art.40
5. Não
6. Sentença de mérito ou definitiva
7. Remessa para o arquivo
8. Unificado
9. Volta da suspensão do artigo 40
10. Citação
11. sentença em apensado.
12. Forma tácita
13. leilão
14. Pelo prazo de 10 dias
15. Arquivo provisório do art. 28 lef
16. Alvará
17. Art. 40
18. Citação de embargos

## Métricas de Avaliação

As métricas abaixo foram calculadas no conjunto de validação:

- **Accuracy:** 0.9352
- **F1-Score (Weighted):** 0.9329
- **Precision (Weighted):** 0.9342
- **Recall (Weighted):** 0.9352
- **F1-Score (Macro):** 0.8260

## Classification Report Completo

```
              precision    recall  f1-score   support

           0       0.33      0.17      0.22         6
           1       0.86      0.96      0.91        46
           2       0.98      0.73      0.83        55
           3       0.89      0.97      0.93        35
           4       0.92      0.95      0.94       132
           5       0.95      0.96      0.95       424
           6       1.00      1.00      1.00        43

    accuracy                           0.94       741
   macro avg       0.85      0.82      0.83       741
weighted avg       0.93      0.94      0.93       741

```

## Uso

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Carregar modelo e tokenizer
model = AutoModelForSequenceClassification.from_pretrained("rkenji/testmodel")
tokenizer = AutoTokenizer.from_pretrained("rkenji/testmodel")

# Fazer predição
texto = "Seu texto jurídico aqui"
inputs = tokenizer(texto, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=1).item()

print(f"Classe predita: {prediction}")
```

## Treinamento

O modelo foi treinado com early stopping e validação cruzada em um dataset de documentos jurídicos brasileiros.


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- **Language(s) (NLP):** pt
- **License:** apache-2.0
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