jingtaozhan / jpq Goto Github PK
View Code? Open in Web Editor NEWCIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.
License: MIT License
CIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.
License: MIT License
Hi @jingtaozhan,
Thanks for your evaluation code for BEIR repository, it certainly helped me a lot.
I have an additional request, I wish to train the JPQ (and RepCONC if possible) model on a custom dataset from the BEIR repository, which contains pairwise question-passage training pairs.
Can this already be done through the repository? Is there a sample code that I can easily refer to?
Thanks!
Kind Regards,
Nandan Thakur
Hello developers,
Could you share the code to run the experiments on the ColBERT-V2 model, as in the paper "Joint Optimization of Multi-vector Representation with Product Quantization"?
Hi all,
Thanks for this interesting work and making the code available. I am Buruo, an MSc student at the University of Glasgow, studying with @cmacdonald. Iโm trying to make a PyTerrier plugin for JPQ.
I am using your pre-built indices, but my evaluation doesn't match those reported in the paper. My best NDCG@10 is 0.5568 on the TREC 2019 Deep Learning track Passage Task.
For us to debug our integration, would you be able to provide results files - i.e. rankings generated by JPQ. This would also be useful for others wishing to compare the JPQ results in their own papers.
Buruo
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