README.md

---
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: 0.9230769230769231
    - type: f1
      value: 0.9213415721061173
    - type: precision
      value: 0.9231096484224747
    - type: recall
      value: 0.9230769230769231
    - type: f1_macro
      value: 0.4819512053123249
---

# 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:** 13
- **Idioma:** Português (Brasil)
- **Domínio:** Documentos Jurídicos

## Classes

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

## Métricas de Avaliação

As métricas abaixo foram calculadas no conjunto de validação:

- **Accuracy:** 0.9231
- **F1-Score (Weighted):** 0.9213
- **Precision (Weighted):** 0.9231
- **Recall (Weighted):** 0.9231
- **F1-Score (Macro):** 0.4820

## Classification Report Completo

```
              precision    recall  f1-score   support

           0       0.60      0.38      0.46         8
           1       0.90      0.94      0.92        47
           2       0.96      0.82      0.88        61
           3       0.90      0.98      0.94        45
           4       0.91      0.97      0.94       140
           5       0.96      0.95      0.95       427
           7       0.00      0.00      0.00         1
           8       0.29      1.00      0.44         2
           9       0.00      0.00      0.00         1
          10       0.33      0.20      0.25         5
          11       0.00      0.00      0.00         2
          12       0.00      0.00      0.00         2

    accuracy                           0.92       741
   macro avg       0.49      0.52      0.48       741
weighted avg       0.92      0.92      0.92       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.


- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** pt
- **License:** apache-2.0
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Glossary [optional]

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