README.md

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}")