Training 20251202_133947 - 7 classes

README.md

@@ -1,33 +1,6 @@
 ---
-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: 0.936231884057971
-    - type: f1
-      value: 0.9353615414017292
-    - type: precision
-      value: 0.9369069235885541
-    - type: recall
-      value: 0.936231884057971
-    - type: f1_macro
-      value: 0.8474441104841182
+library_name: transformers
+tags: []
 ---
 
 # Model Card for Model ID
@@ -42,86 +15,14 @@ model-index:
 
 <!-- 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:** 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
-
-```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.
-
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
-- **Language(s) (NLP):** pt
-- **License:** apache-2.0
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
 - **Finetuned from model [optional]:** [More Information Needed]
 
 ### Model Sources [optional]

config.json

@@ -10,22 +10,23 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "646bb41b351734d0a39bb557",
-    "1": "652026c3798f7600075f9d40",
-    "2": "646bc57bc7605185f40b4263",
-    "3": "646b78c3dc761bc8c743a0c6",
-    "4": "645d42ebbacbc7baba32624d",
-    "5": "64ac68185fe31ca8ad57a822"
+    "0": "Alvar\u00e1",
+    "1": "Senten\u00e7a",
+    "2": "Penhora/Arresto/Sequestro",
+    "3": "Cita\u00e7\u00e3o",
+    "4": "Art.40",
+    "5": "N\u00e3o",
+    "6": "Alvar\u00e1"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "645d42ebbacbc7baba32624d": 4,
-    "646b78c3dc761bc8c743a0c6": 3,
-    "646bb41b351734d0a39bb557": 0,
-    "646bc57bc7605185f40b4263": 2,
-    "64ac68185fe31ca8ad57a822": 5,
-    "652026c3798f7600075f9d40": 1
+    "Alvar\u00e1": 6,
+    "Art.40": 4,
+    "Cita\u00e7\u00e3o": 3,
+    "N\u00e3o": 5,
+    "Penhora/Arresto/Sequestro": 2,
+    "Senten\u00e7a": 1
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,

model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ebbc0bde22fe9605d6b416bb40abfd1a9c25bb23293ebd059249447c5a5db0fb
-size 711455760

special_tokens_map.json

@@ -1,7 +1,37 @@
 {
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
 }