Update README.md

README.md

@@ -43,14 +43,11 @@ def get_sentence_embedding(title, abstract, model, tokenizer, max_length=None):
     sentence = '</s>'.join([title, abstract])
     encoded_input = tokenizer(
         [sentence], padding=True, truncation=True, return_tensors='pt', max_length=max_length).to(model.device)
-
     # Compute token embeddings
     with torch.no_grad():
         model_output = model(**encoded_input)
-
     # Perform pooling
     sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-    
     # Normalize embeddings
     sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
     return sentence_embeddings.cpu().detach().numpy()[0]