Training 20251203_145320 - 23 classes with metrics

6aa7e12 9 months ago

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	---
	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: 0.8881829733163914
	- type: f1
	value: 0.8777856821629471
	- type: precision
	value: 0.8772538874253621
	- type: recall
	value: 0.8881829733163914
	- type: f1_macro
	value: 0.3692552572091707
	---

	# Model Card for Model ID

	<!-- Provide a quick summary of what the model is/does. -->



	## Model Details

	### Model Description

	<!-- Provide a longer summary of what this model is. -->


	# 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: 23
	- Idioma: Português (Brasil)
	- Domínio: Documentos Jurídicos

	## Classes

	0. Alvará
	1. Sentença
	2. Penhora/Arresto/Sequestro
	3. Citação
	4. Art.40
	5. Não
	6. Pelo prazo de 10 dias
	7. Sentença de mérito ou definitiva
	8. Citação de embargos
	9. Forma tácita
	10. Citação de penhora
	11. Art. 40
	12. sentença em apensado.
	13. Unificado
	14. Citação Negativa
	15. Arquivo provisório do art. 28 lef
	16. leilão
	17. intimação
	18. Citação de arresto
	19. Citação Positiva
	20. Penhora
	21. Desentramento
	22. Remessa para o arquivo

	## Métricas de Avaliação

	As métricas abaixo foram calculadas no conjunto de validação:

	- Accuracy: 0.8882
	- F1-Score (Weighted): 0.8778
	- Precision (Weighted): 0.8773
	- Recall (Weighted): 0.8882
	- F1-Score (Macro): 0.3693

	## Classification Report Completo

	```
	precision recall f1-score support

	0 0.00 0.00 0.00 7
	1 0.96 0.94 0.95 51
	2 0.93 0.88 0.91 49
	3 0.82 0.88 0.85 42
	4 0.94 0.95 0.94 138
	5 0.96 0.95 0.96 414
	6 0.00 0.00 0.00 7
	7 1.00 1.00 1.00 16
	8 0.00 0.00 0.00 4
	9 0.40 1.00 0.57 6
	10 0.08 1.00 0.15 2
	11 0.67 1.00 0.80 10
	12 0.00 0.00 0.00 8
	13 0.53 0.91 0.67 11
	14 0.00 0.00 0.00 3
	15 0.00 0.00 0.00 2
	16 0.00 0.00 0.00 1
	17 0.00 0.00 0.00 6
	19 0.00 0.00 0.00 3
	20 1.00 0.20 0.33 5
	21 0.00 0.00 0.00 1
	22 0.00 0.00 0.00 1

	accuracy 0.89 787
	macro avg 0.38 0.44 0.37 787
	weighted avg 0.88 0.89 0.88 787

	```

	## 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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