spacemanidol commited on
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1 Parent(s): d6dfa79

Update README.md

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  1. README.md +3 -3
README.md CHANGED
@@ -2997,14 +2997,14 @@ document_tokens = tokenizer(documents, padding=True, truncation=True, return_te
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  # Compute token embeddings
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  with torch.no_grad():
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  query_embeddings = model(**query_tokens)[0][:, 0]
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- doument_embeddings = model(**document_tokens)[0][:, 0]
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  # normalize embeddings
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  query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
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- doument_embeddings = torch.nn.functional.normalize(doument_embeddings, p=2, dim=1)
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- scores = torch.mm(query_embeddings, doument_embeddings.transpose(0, 1))
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  for query, query_scores in zip(queries, scores):
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  doc_score_pairs = list(zip(documents, query_scores))
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  doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
 
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  # Compute token embeddings
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  with torch.no_grad():
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  query_embeddings = model(**query_tokens)[0][:, 0]
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+ document_embeddings = model(**document_tokens)[0][:, 0]
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  # normalize embeddings
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  query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
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+ document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
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+ scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
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  for query, query_scores in zip(queries, scores):
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  doc_score_pairs = list(zip(documents, query_scores))
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  doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)