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Update README.md

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@@ -94,7 +94,6 @@ inputs = {key: value.to(device) for key, value in inputs.items()}
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  with torch.no_grad():
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  embeddings_dict = model(inputs)
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  embeddings = embeddings_dict['token_embeddings']
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- print(embeddings.shape) # Expected: [3, 32, 128] (3 texts, 32 tokens max, 128-dim embeddings)
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  # Define ColBERT's MaxSim similarity function
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  def colbert_similarity(query_emb, doc_emb):
 
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  with torch.no_grad():
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  embeddings_dict = model(inputs)
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  embeddings = embeddings_dict['token_embeddings']
 
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  # Define ColBERT's MaxSim similarity function
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  def colbert_similarity(query_emb, doc_emb):