Closed Duemoo closed 1 year ago
Hi @Duemoo, you may refer to this pull request to fix the issue for reranking.
I also cannot reproduce the 64-shot results reported in Table 2 with the pretraining/finetuning script and the settings described in the paper (without reranking in pretraining).
Thank you! I applied your commits and it fixed the problem :)
@jeffhj Thank you for the information. I guess that using CCNet indices together could be an important factor for the performance, and so I'm trying to reproduce the results using CCNet texts as well.
Thank you @jeffhj for the fix, I've merged it into master, so I am closing this issue.
Hello, I was trying to pre-train the ATLAS model (base & large size), by running the provided example script in atlas/example_scripts/mlm/train.sh with 4 40GB A100 GPUs, but then I got this error:
I used the provided passages (Wikipedia Dec2018 dump), and ran the script without any changes in training arguments. So, the batch size per device was 2 and 100 documents were retrieved by the retriever, regarding
[2, 768]->[2, 1, 768] [2, 100, 1536]->[2, 100, 1536]
in the error message above. In addition, I found that the script and the overall pre-training process worked well after removing this line from the script, i.e., doing re-indexing of the whole passages instead of doing re-ranking, although this resulted in lower few-shot performance compared to the scores reported in Table.19 from the paper. (However, I think the performance issue might be irrelevant to the removal of this line)--retrieve_with_rerank --n_to_rerank_with_retrieve_with_rerank 100 \
Could you provide any hints to solve this issue? Thank you in advance!