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Update README.md

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@@ -29,6 +29,7 @@ pip install -U sentence-transformers
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  Then you can use the model like this:
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  ```python
 
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  from sentence_transformers import SentenceTransformer, util
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  model = SentenceTransformer("Huffon/sentence-klue-roberta-base")
@@ -52,11 +53,11 @@ cos_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)[0]
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  top_results = torch.topk(cos_scores, k=top_k)
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  print(f"입력 문장: {query}")
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- print(f"\
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- <입력 문장과 유사한 {top_k} 개의 문장>\
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  ")
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  for i, (score, idx) in enumerate(zip(top_results[0], top_results[1])):
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- print(f"{i+1}: {docs[idx]} {'(유사도: {:.4f})'.format(score)}\
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  ")
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  ```
 
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  Then you can use the model like this:
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  ```python
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+ import torch
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  from sentence_transformers import SentenceTransformer, util
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  model = SentenceTransformer("Huffon/sentence-klue-roberta-base")
 
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  top_results = torch.topk(cos_scores, k=top_k)
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  print(f"입력 문장: {query}")
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+ print(f"\\
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+ <입력 문장과 유사한 {top_k} 개의 문장>\\
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  ")
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  for i, (score, idx) in enumerate(zip(top_results[0], top_results[1])):
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+ print(f"{i+1}: {docs[idx]} {'(유사도: {:.4f})'.format(score)}\\
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  ")
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  ```