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Update README.md

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  1. README.md +3 -6
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@@ -82,14 +82,10 @@ sim = model.similarity(query_embed, document_embed)
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  print(f"Similarity: {sim}")
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  # Similarity: tensor([[7.7400]])
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- # Visualize top tokens for each text
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- top_k = 5
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- print(f"\nTop tokens {top_k} for each text:")
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-
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- decoded_query = model.decode(query_embed, top_k=top_k)
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  decoded_document = model.decode(document_embed)
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- for i in range(top_k):
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  query_token, query_score = decoded_query[i]
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  doc_score = next((score for token, score in decoded_document if token == query_token), 0)
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  if doc_score != 0:
@@ -100,6 +96,7 @@ for i in range(top_k):
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  # Token: now, Query score: 1.6406, Document score: 0.9018
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  # Token: ?, Query score: 1.6108, Document score: 0.3141
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  # Token: ny, Query score: 1.2721, Document score: 1.3446
 
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  ```
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  ## Usage (HuggingFace)
 
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  print(f"Similarity: {sim}")
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  # Similarity: tensor([[7.7400]])
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+ decoded_query = model.decode(query_embed)
 
 
 
 
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  decoded_document = model.decode(document_embed)
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+ for i in range(len(decoded_query)):
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  query_token, query_score = decoded_query[i]
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  doc_score = next((score for token, score in decoded_document if token == query_token), 0)
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  if doc_score != 0:
 
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  # Token: now, Query score: 1.6406, Document score: 0.9018
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  # Token: ?, Query score: 1.6108, Document score: 0.3141
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  # Token: ny, Query score: 1.2721, Document score: 1.3446
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+ # Token: in, Query score: 0.6005, Document score: 0.1804
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  ```
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  ## Usage (HuggingFace)