halilibr/dilbazlar-binary-disorder-detection-model-acc-92

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

loaded_model = AutoModelForSequenceClassification.from_pretrained("halilibr/dilbazlar-binary-disorder-detection-model-acc-92", num_labels=2)
tokenizer = AutoTokenizer.from_pretrained("halilibr/dilbazlar-binary-disorder-detection-model-acc-92")

# Move the model to the appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
loaded_model.to(device)

# Ensure model is in evaluation mode
loaded_model.eval()

# Example input
input_text = "kendimi kötü hissediyorum"

# Tokenize the input (ensure the tokenizer is appropriate for your model)
inputs = tokenizer(input_text, max_length=150, padding="max_length", truncation=True, return_tensors="pt")

# Move the inputs to the appropriate device
inputs = {k: v.to(device) for k, v in inputs.items()}

print(inputs)

# Disable gradient computation for inference
with torch.no_grad():
    # Forward pass to get outputs
    outputs = loaded_model(**inputs)
    
    # Get the prediction
    # Note: `AutoModel` might not include logits. Ensure you use the appropriate model class for your task.
    if hasattr(outputs, 'logits'):
        preds = torch.argmax(outputs.logits, dim=-1)
    else:
        # Handle the case where the model does not have logits (e.g., outputs are raw hidden states)
        preds = torch.argmax(outputs[0], dim=-1)

# Convert prediction to numpy array and print (if needed)
prediction = preds.cpu().numpy()[0]

# Print the predicted class
print("Predicted class:", prediction)