rag-reranking-gpt-colbert.ipynb

rag-reranking-gpt-colbert.ipynb

thanks for sharing but the query_embedding variable missing assignment statement

@truebit If I have done it right you need to add:

# Add this lines
query = "Your query in string format..."
query_encoding = tokenizer(query, return_tensors='pt', truncation=True, max_length=512)
query_embedding = model(**query_encoding).last_hidden_state.squeeze(0)

# Get score for each document
for document in splits:
    document_encoding = tokenizer(document, return_tensors='pt', truncation=True, max_length=512)
    document_embedding = model(**document_encoding).last_hidden_state

    # Calculate MaxSim score
    score = maxsim(query_embedding.unsqueeze(0), document_embedding)
    ...

@Psancs05 thx

Great catch - updated 🙏

@virattt Do you know the difference between using:
query_embedding = model(**query_encoding).last_hidden_state.squeeze(0)
query_embedding = model(**query_encoding).last_hidden_state.mean(dim=1)

I have tested both and seems that the squeeze(0) returns better quality similar documents (maybe it's just the use-case I tried)

query_embedding = model(**query_encoding).last_hidden_state.squeeze(0) is correct since it returns a vector per token, whilst
query_embedding = model(**query_encoding).last_hidden_state.mean(dim=1) returns a single vector averaged over all tokens.