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Update README.md

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  1. README.md +16 -4
README.md CHANGED
@@ -117,10 +117,16 @@ try:
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  "Deep learning uses neural networks with multiple layers to process data.",
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  "Neural networks are computing systems inspired by biological neural networks.",
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  "Artificial intelligence encompasses machine learning and deep learning.",
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- "Here is how you train dogs",
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  ]
 
 
 
 
 
 
 
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- # Test single query ranking
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  print("\n=== Single Query Ranking ===")
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  query = "How does deep learning work?"
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  results = colbert.rank_documents(query, documents, top_k=3)
@@ -501,10 +507,16 @@ documents = [
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  "Deep learning uses neural networks with multiple layers to process data.",
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  "Neural networks are computing systems inspired by biological neural networks.",
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  "Artificial intelligence encompasses machine learning and deep learning.",
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- "Here is how you train dogs",
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  ]
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- # Test single query ranking
 
 
 
 
 
 
 
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  print("\n=== ONNX Standalone Single Query Ranking ===")
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  query = "How does deep learning work?"
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  results = onnx_colbert.rank_documents(query, documents, top_k=3)
 
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  "Deep learning uses neural networks with multiple layers to process data.",
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  "Neural networks are computing systems inspired by biological neural networks.",
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  "Artificial intelligence encompasses machine learning and deep learning.",
 
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  ]
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+
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+ # Encode and find similarity
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+ q_reps = colbert.encode_queries(queries, batch_size=4, to_cpu=True)
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+ p_reps = colbert.encode_documents(documents, batch_size=4, to_cpu=True)
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+
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+ print("Computing similarities...")
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+ scores = colbert.compute_similarity(q_reps, p_reps)
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+ # or Test single query ranking
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  print("\n=== Single Query Ranking ===")
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  query = "How does deep learning work?"
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  results = colbert.rank_documents(query, documents, top_k=3)
 
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  "Deep learning uses neural networks with multiple layers to process data.",
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  "Neural networks are computing systems inspired by biological neural networks.",
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  "Artificial intelligence encompasses machine learning and deep learning.",
 
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  ]
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+ # Encode and find similarity
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+ q_reps = onnx_colbert.encode_queries(queries, batch_size=4, to_cpu=True)
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+ p_reps = onnx_colbert.encode_documents(documents, batch_size=4, to_cpu=True)
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+ print("Computing similarities...")
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+ scores = onnx_colbert.compute_similarity(q_reps, p_reps)
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+
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+
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+ # or Test single query ranking
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  print("\n=== ONNX Standalone Single Query Ranking ===")
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  query = "How does deep learning work?"
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  results = onnx_colbert.rank_documents(query, documents, top_k=3)