Create README.md

README.md

@@ -0,0 +1,37 @@
+# EJEMPLO DE USO
+
+
+
+## Cargar librerías
+
+import torch
+from transformers import XLMRobertaForSequenceClassification, XLMRobertaTokenizer, AutoTokenizer
+
+## Cargar el modelo y el tokenizador
+model_path = "nmarinnn/bert-bregman"
+model = XLMRobertaForSequenceClassification.from_pretrained(model_path)
+tokenizer = XLMRobertaTokenizer.from_pretrained(model_path)
+loaded_tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+## Función para predecir etiqueta
+
+def predict(text):
+    inputs = loaded_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+    
+    class_labels = {0: "negativo", 1: "neutro", 2: "positivo"}
+    predicted_label = class_labels[predicted_class]
+    predicted_probability = probabilities[0][predicted_class].item()
+    
+    return predicted_label, predicted_probability, probabilities[0].tolist()
+
+# Ejemplo de uso
+text_to_classify = "vamos rusa"
+predicted_label, predicted_prob, class_probabilities = predict(text_to_classify)
+
+print(f"Clase predicha: {predicted_label} (probabilidad = {predicted_prob:.2f})")
+print(f"Probabilidades de todas las clases: Negativo: {class_probabilities[0]:.2f}, Neutro: {class_probabilities[1]:.2f}, Positivo: {class_probabilities[2]:.2f}")