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vec2.squeeze() return torch.dot(vec1, vec2) / (torch.norm(vec1) * torch.norm(vec2)) # Define the sentence_to_embeddings function def sentence_to_embeddings(sentence, model, tokenizer): inputs = tokenizer(sentence, return_tensors='pt') with torch.no_grad(): outputs = model(**inputs) embeddings =