rkenji/testmodel

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Model Details

Model Description

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

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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Recommendations

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

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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