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Use updated path to MiniLM bi-encoder model on hugging-face
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1 changed files with 3 additions and 3 deletions
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@ -14,9 +14,9 @@ import pathlib
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def initialize_model():
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"Initialize model for assymetric semantic search. That is, where query smaller than results"
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bi_encoder = SentenceTransformer('msmarco-MiniLM-L-6-v3') # The bi-encoder encodes all entries to use for semantic search
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top_k = 100 # Number of entries we want to retrieve with the bi-encoder
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cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') # The cross-encoder re-ranks the results to improve quality
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bi_encoder = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L-6-v3') # The bi-encoder encodes all entries to use for semantic search
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top_k = 100 # Number of entries we want to retrieve with the bi-encoder
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cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') # The cross-encoder re-ranks the results to improve quality
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return bi_encoder, cross_encoder, top_k
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