|
# 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}") |