Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Training settings #7

Open
wahr0411 opened this issue Mar 28, 2022 · 3 comments
Open

Training settings #7

wahr0411 opened this issue Mar 28, 2022 · 3 comments

Comments

@wahr0411
Copy link

Hello, I'd like to ask how many epochs did you run to reach 90.5% F1-score on RNAStralign, and on other datasets? And did you adjust the learning rate while training? How can I get the pretrained model mentioned in your paper (by using which datasets how many epochs)?

Thanks

@sperfu
Copy link
Contributor

sperfu commented Apr 10, 2022

Hi there,

I believe we run for about 100 epochs to get the score. As for other datasets, we can not guarantee all the epochs are the same because sometimes the 80 + epochs may already reach a plateau so there's no need for further training, so we stopped the training if training loss did not change much. We did not change the learning rate during training. All the pretrained models are stored in our cloud drive, please check it out in the font page in our github Readme file.

Thanks

@wahr0411
Copy link
Author

Thanks very much for your reply!

@wahr0411
Copy link
Author

wahr0411 commented Apr 14, 2022

I'd also like to ask how much time did you cost for training each epoch? I run it on one GTX 3090 with batchsize=1, it cost about 2 hours to train an epoch (slower than I expect). I wonder if I did something wrong while training, because the model seems not so complex.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants