from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "SCM-LAB/fluency-phobert-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
text = "Paris nằm ở chỗ nào?"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
predicted_probabilities = torch.softmax(outputs.logits, dim=1)
predicted_probabilities = predicted_probabilities.tolist()[0]
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print("Nonfluency", predicted_probabilities[0])
print("Fluency" , predicted_probabilities[1])
