[NewOp] Add generate_challenging_qa_mapper based on MindGYM principles #703
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
cyruszhang
reviewed
Jun 15, 2025
| from data_juicer.utils.lazy_loader import LazyLoader | ||
| from data_juicer.utils.model_utils import get_model, prepare_model | ||
|
|
||
| torch = LazyLoader('torch', 'torch') |
There was a problem hiding this comment.
can import from model_utils: torch, vllm
| OP_NAME = 'generate_challenging_qa_mapper' | ||
|
|
||
|
|
||
| def retry_on_error(func, max_retries=5, delay=1): |
There was a problem hiding this comment.
can use existing third party retry library or put retry_on_error in a util
|
Please merge the latest main branch and run pre-commit locally. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Introduces a novel QA generation module based on self-challenging mechanisms, designed to autonomously synthesize high-quality reasoning-focused question-answer pairs, inspired by the MindGYM paper.