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: 0.9124293785310734
- type: f1
value: 0.9069008258581538
- type: precision
value: 0.9052447065095824
- type: recall
value: 0.9124293785310734
- type: f1_macro
value: 0.6832937813302449
Model Card for Model ID
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: 8
- Idioma: Português (Brasil)
- Domínio: Documentos Jurídicos
Classes
- Alvará
- Sentença
- Penhora/Arresto/Sequestro
- Citação
- Art.40
- Não
- Teste
- Teste 2
Métricas de Avaliação
As métricas abaixo foram calculadas no conjunto de validação:
- Accuracy: 0.9124
- F1-Score (Weighted): 0.9069
- Precision (Weighted): 0.9052
- Recall (Weighted): 0.9124
- F1-Score (Macro): 0.6833
Classification Report Completo
precision recall f1-score support
0 0.00 0.00 0.00 7
1 0.94 0.98 0.96 48
2 0.95 0.72 0.82 57
3 0.82 0.95 0.88 42
4 0.88 0.95 0.91 132
5 0.94 0.95 0.94 408
6 0.40 0.33 0.36 6
7 0.56 0.62 0.59 8
accuracy 0.91 708
macro avg 0.69 0.69 0.68 708
weighted avg 0.91 0.91 0.91 708
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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