| ```python | |
| 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] # Chuyển tensor thành list | |
| predicted_class = torch.argmax(outputs.logits, dim=1).item() | |
| print("Nonfluency", predicted_probabilities[0]) | |
| print("Fluency" , predicted_probabilities[1]) | |
| ``` |