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--- |
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language: |
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- en |
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metrics: |
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- f1 |
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pipeline_tag: text-classification |
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--- |
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# Initialize tokenizer and model |
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tokenizer = BartTokenizer.from_pretrained('ihgn/paraphrase-detection') |
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model = BartForConditionalGeneration.from_pretrained("ihgn/paraphrase-detection").to(device) |
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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." |
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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" |
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def paraphrase_detection(model, tokenizer, source_sentence, target_paraphrase): |
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# Tokenize the input sentence |
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inputs = tokenizer.encode_plus(source_sentence + ' <sep> ' + target_paraphrase, return_tensors='pt') |
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# Classify the input using the model |
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with torch.no_grad(): |
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outputs = model.generate(inputs['input_ids'].to(device)) |
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# Get the predicted label |
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predicted_label = 1 if generated_text == '1' else 0 |
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print("Predicted Label:", predicted_label) |
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paraphrase_detection(model, tokenizer, source_sentence, target_paraphrase) |
