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License: Apache License 2.0
ConTextual Mask Auto-Encoder for Dense Passage Retrieval
License: Apache License 2.0
Thank you for your excellent work. I am replicating the data processing steps of your cotmae-qc project and encountered an error in the script found in the qg directory's generate.py, due to the missing util module. Could you please upload that file?
It seems that there is only loss_ab in the released code, right?
Hi, I want to reproduce the results on NQ, but I don't see hyperparameter setting in paper or repo. Do I just need to follow the script msmarco/eval_msmarco.sh and modify the augments to reproduce? Can you provide more instruction? It would be great if you can provide the model weight of retrievers on two stages when finetuning on NQ. Thanks in advance!
Hi @ma787639046, thanks for opensouring the interesting work!
I have some questions about the reranker training details.
In your paper, the reported the reranker MRR@10 is 43.3. While in the hf model card, the reported MRR@10 is 43.9.
Did you use different base model for initialization? Or different hyperparameters? Would you mind sharding more training details because I didn't find it in your paper nor in the repo...
Thanks!
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