Initialize tokenizer and model

tokenizer = BartTokenizer.from_pretrained('ihgn/paraphrase-detection')
model = BartForConditionalGeneration.from_pretrained("ihgn/paraphrase-detection").to(device)
source_sentence = "This was a series of nested angular standards , so that measurements in azimuth and elevation could be done directly in polar coordinates relative to the ecliptic."
target_paraphrase = "This was a series of nested polar scales , so that measurements in azimuth and elevation could be performed directly in angular coordinates relative to the ecliptic"

def paraphrase_detection(model, tokenizer, source_sentence, target_paraphrase):
  # Tokenize the input sentence
  inputs = tokenizer.encode_plus(source_sentence + ' <sep> ' + target_paraphrase, return_tensors='pt')

  # Classify the input using the model
  with torch.no_grad():
      outputs = model.generate(inputs['input_ids'].to(device))

  # Get the predicted label
  predicted_label = 1 if generated_text == '1' else 0
  print("Predicted Label:", predicted_label)
paraphrase_detection(model, tokenizer, source_sentence, target_paraphrase)