sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
The tokenizer will automatically add any model specific separators (i.e.  and ) and tokens to
the sequence, as well as compute the attention masks.
paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")
not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")
paraphrase_classification_logits = model(paraphrase).logits
not_paraphrase_classification_logits = model(not_paraphrase).logits
paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]
not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]
Should be paraphrase
for i in range(len(classes)):
...     print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")
not paraphrase: 10%
