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

About the results of this model #2

Open
qingyue2014 opened this issue Jun 8, 2021 · 10 comments
Open

About the results of this model #2

qingyue2014 opened this issue Jun 8, 2021 · 10 comments

Comments

@qingyue2014
Copy link

Hi, I rerun this project with the same experimental setting. But the joint accuracy only achieves around 55% on multiwoz2.1 dataset. Is there any special version of the transformers or something?

@smartyfh
Copy link
Owner

smartyfh commented Jun 8, 2021

Hi, I rerun this project with the same experimental setting. But the joint accuracy only achieves around 55% on multiwoz2.1 dataset. Is there any special version of the transformers or something?

You can try the checkpoint I provided

@qingyue2014
Copy link
Author

I try the checkpoint you provided and it achieves the result paper reporting. But I rerun the project several times still can't gain the best model(more than one percent drop). Would you recheck that the hyper-param settings shown in the GitHub is completely same to that in provided best model ? Thx.

@smartyfh
Copy link
Owner

smartyfh commented Jun 9, 2021

I try the checkpoint you provided and it achieves the result paper reporting. But I rerun the project several times still can't gain the best model(more than one percent drop). Would you recheck that the hyper-param settings shown in the GitHub is completely same to that in provided best model ? Thx.

The parameters are the same. The performance could be affected by the GPUs you used.

@AlbertChen1991
Copy link

I was also unable to reproduce the result, only get 54.3 on MultiWOZ 2.1 dataset

@qingyue2014
Copy link
Author

I was also unable to reproduce the result, only get 54.3 on MultiWOZ 2.1 dataset

I get 55.04 on 2.1 hhh

@smartyfh
Copy link
Owner

I was also unable to reproduce the result, only get 54.3 on MultiWOZ 2.1 dataset

I get 55.04 on 2.1 hhh

Thanks for your interest. Please check the training records here: https://github.com/smartyfh/DST-STAR/blob/main/out-bert/exp/exp.txt

@couragelfyang
Copy link

@smartyfh
Thanks for your interesting work. I've compare https://github.com/smartyfh/DST-STAR/blob/main/out-bert/exp/exp.txt and the results reported in your paper. Is the "slot_acc" same to the "Appendix A slot-specific Accuracy"? Why are they different in results.

@smartyfh
Copy link
Owner

@smartyfh
Thanks for your interesting work. I've compare https://github.com/smartyfh/DST-STAR/blob/main/out-bert/exp/exp.txt and the results reported in your paper. Is the "slot_acc" same to the "Appendix A slot-specific Accuracy"? Why are they different in results.

The reported results are the average of multiple random seeds. So is the joint goal accuracy.

@Janeey99
Copy link

i got 55.79% on multiwoz2.1 with random seed=42, and i only change batch_size to 8, other params are same as the author's. i dont know why.........

@smartyfh
Copy link
Owner

i got 55.79% on multiwoz2.1 with random seed=42, and i only change batch_size to 8, other params are same as the author's. i dont know why.........

Hi, thank you for the feedback, batch size can indeed affect model performance, it is good to know that the model works when setting the batch size to 8.

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

5 participants