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

Could you provide a detailed instruction #1

Closed
StevenTang1998 opened this issue May 11, 2022 · 2 comments
Closed

Could you provide a detailed instruction #1

StevenTang1998 opened this issue May 11, 2022 · 2 comments

Comments

@StevenTang1998
Copy link

Hello, congratulations on your work being accepted for ACL 2022!

I want to follow your work and reproduce the results. However, the script you provide didn't run successfully in my environment. There seems to be some local paths, and the location and format of the dataset are not particularly clear.

Could you offer some help and wish you a happy life!

@Ravoxsg
Copy link
Owner

Ravoxsg commented May 17, 2022

Hi @StevenTang1998 , thanks!

So in order to run the re-ranker, you need to first generate a set of summary candidates
This is done in src/candidate_generation/main_candidate_generation.py
This will generate num_beams candidate for each source document in the dataset.

Then, you need to score these candidates to evaluate the re-ranking.
This is done in src/candidate_generation/main_scores.py

Let me know if you need any help running these scripts.

@Ravoxsg
Copy link
Owner

Ravoxsg commented May 21, 2022

@StevenTang1998 Please follow instructions from the other similar issue:
#2

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