| | --- |
| | pipeline_tag: text-classification |
| | --- |
| | 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) |
