| # 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}") |