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Upload 16 files (#24)
Browse files- Upload 16 files (897ff3ac01d97606a3afc669b62e17125ad3aaf8)
app.py
CHANGED
@@ -94,14 +94,6 @@ with st.sidebar:
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data = get_data()
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corpus, bm25 = get_bm25_model(data)
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tokenized_query = preprocess_text(query_text).split()
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sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1]
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indices_hits = get_bm25_search_hits(corpus, sparse_scores, 50)
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if use_bm25 == True:
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indices = indices_hits
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else:
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indices = None
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if ner_choice == "Spacy":
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ner_model = get_spacy_model()
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@@ -128,6 +120,16 @@ with col1:
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)
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# Extract keywords from query
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keywords = extract_keywords(query_text, alpaca_model)
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@@ -383,7 +385,6 @@ with st.sidebar:
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)
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if document_type == "Single-Document":
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if encoder_model in ["Hybrid SGPT - SPLADE", "Hybrid Instructor - SPLADE"]:
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if encoder_model == "Hybrid Instructor - SPLADE":
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data = get_data()
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corpus, bm25 = get_bm25_model(data)
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if ner_choice == "Spacy":
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ner_model = get_spacy_model()
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)
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tokenized_query = preprocess_text(query_text).split()
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sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1]
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indices_hits = get_bm25_search_hits(corpus, sparse_scores, 50)
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if use_bm25 == True:
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indices = indices_hits
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else:
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indices = None
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# Extract keywords from query
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keywords = extract_keywords(query_text, alpaca_model)
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)
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if document_type == "Single-Document":
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if encoder_model in ["Hybrid SGPT - SPLADE", "Hybrid Instructor - SPLADE"]:
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if encoder_model == "Hybrid Instructor - SPLADE":
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