Add examples that showcase the use of Hyperparameter search with Transformers #589
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
|
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.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.
This PR closes issue #37947 by expanding our hyperparameter‑search examples in the Trainer docs. In particular, it:
• Adds a SigOpt example (showing double, int, and categorical params)
• Includes a W&B example with random search configuration
• Ensures all four backends (Optuna, Ray Tune, SigOpt, W&B) follow the same structure
• Updates the setup instructions to list required dependencies
I’ve linked the original issue for context: huggingface/transformers#37947
Please let me know if anything needs tweaking—happy to adjust formatting, add more comments or improve the compute_objective logic! Thanks for taking a look. 🚀