---
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: 0.936231884057971
    - type: f1
      value: 0.9353615414017292
    - type: precision
      value: 0.9369069235885541
    - type: recall
      value: 0.936231884057971
    - type: f1_macro
      value: 0.8474441104841182
---

# 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:** 6
- **Idioma:** Português (Brasil)
- **Domínio:** Documentos Jurídicos

## Classes

0. Alvará
1. Sentença
2. Penhora
3. Citação
4. Art. 40
5. Não Influencia

## Métricas de Avaliação

As métricas abaixo foram calculadas no conjunto de validação:

- **Accuracy:** 0.9362
- **F1-Score (Weighted):** 0.9354
- **Precision (Weighted):** 0.9369
- **Recall (Weighted):** 0.9362
- **F1-Score (Macro):** 0.8474

## Classification Report Completo

```
              precision    recall  f1-score   support

           0       0.60      0.38      0.46         8
           1       0.86      0.98      0.92        45
           2       1.00      0.85      0.92        54
           3       0.85      0.95      0.90        41
           4       0.93      0.95      0.94       121
           5       0.95      0.95      0.95       421

    accuracy                           0.94       690
   macro avg       0.87      0.84      0.85       690
weighted avg       0.94      0.94      0.94       690

```

## 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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