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.

Sign up for GitHub

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

Set greater_is_better to False if metric_for_best_model ends with "loss" #31142

Merged
merged 2 commits into from
Jun 3, 2024
Merged

Set greater_is_better to False if metric_for_best_model ends with "loss" #31142

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
7bac7a8
update to not(endswith(loss))
miivanov90 May 30, 2024
27cc9ff
ruff formatting
miivanov90 May 30, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions src/transformers/training_args.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -464,8 +464,8 @@ class TrainingArguments:
Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models
should have a greater metric or not. Will default to:

- `True` if `metric_for_best_model` is set to a value that isn't `"loss"` or `"eval_loss"`.
- `False` if `metric_for_best_model` is not set, or set to `"loss"` or `"eval_loss"`.
- `True` if `metric_for_best_model` is set to a value that doesn't end in `"loss"`.
- `False` if `metric_for_best_model` is not set, or set to a value that ends in `"loss"`.
ignore_data_skip (`bool`, *optional*, defaults to `False`):
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step
Expand Down Expand Up @@ -1582,7 +1582,7 @@ def __post_init__(self):
) and self.metric_for_best_model is None:
self.metric_for_best_model = "loss"
if self.greater_is_better is None and self.metric_for_best_model is not None:
self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"]
self.greater_is_better = not (self.metric_for_best_model.endswith("loss"))
if self.run_name is None:
self.run_name = self.output_dir
if self.framework == "pt" and is_torch_available():
Expand Down
Loading