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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-casedTipo: Classificação MulticlasseNúmero de Classes: 6Idioma: Português (Brasil)Domínio: Documentos Jurídicos
Classes
Alvará
Sentença
Penhora
Citação
Art. 40
Não Influencia
Métricas de Avaliação
As métricas abaixo foram calculadas no conjunto de validação:
Accuracy: 0.9362F1-Score (Weighted): 0.9354Precision (Weighted): 0.9369Recall (Weighted): 0.9362F1-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
1 from transformers import AutoTokenizer , AutoModelForSequenceClassification
2 import torch
3
4 # Carregar modelo e tokenizer
5 model = AutoModelForSequenceClassification . from_pretrained ( "rkenji/testmodel" )
6 tokenizer = AutoTokenizer . from_pretrained ( "rkenji/testmodel" )
7
8 # Fazer predição
9 texto = "Seu texto jurídico aqui"
10 inputs = tokenizer ( texto , return_tensors = "pt" , truncation = True , max_length = 512 )
11 outputs = model ( ** inputs )
12 prediction = torch . argmax ( outputs . logits , dim = 1 ) . item ( )
13
14 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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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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