In train.py test() process, almost every output for pred_answer is a single character like 'e'

[Wu-tn]

Hi,
I met the question in test() process.

[Wu-tn]

Hi,
I found another question that in inference_dense.py , faiss generate almost the same 100 passages for every question in train.json, I follow your steps in train_dense.py that install Luyu/co-condenser-wiki in hugginface and train it with the wikipedia-nq in https://github.com/luyug/Dense , I wonder which step that i make a mistake?

[sunnweiwei]

Hi! It's strange that the retrieval results are the same. Maybe you could try using this model (https://huggingface.co/Luyu/co-condenser-marco-retriever) to run dense retrieval inference and see if the results are normal?

[Wu-tn]

Hi,
It is necessary to train the pre-trained co-condenser model on wikipedia-nq dataset or directly use it to encode corpus and query?

[sunnweiwei]

This model (https://huggingface.co/Luyu/co-condenser-marco-retriever) has been trained on MS MARCO, so can directly be used to encode the corpus and the query.

[Wu-tn]

Thanks, I will try it!!!

[Wu-tn]

By the way, it is possible to provide the 9.pt for download?