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Add tip for logging evaluation metrics during regular evaluations #4367

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Add tip for logging evaluation metrics during regular evaluations #4367

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15 changes: 15 additions & 0 deletions trl/scripts/dpo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,6 +157,21 @@ def main(script_args, training_args, model_args, dataset_args):
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# 💡 Tip: To log and save evaluation metrics during regular evaluations (not only the final one),
# you can use a custom callback:
#
# from transformers import TrainerCallback
#
# class LogEvalMetricsCallback(TrainerCallback):
# def on_evaluate(self, args, state, control, metrics=None, **kwargs):
# if metrics:
# trainer.log_metrics("eval", metrics)
# trainer.save_metrics("eval", metrics)
#
# trainer = Trainer(..., callbacks=[LogEvalMetricsCallback])
#
# Note: Metrics logged to Weights & Biases (W&B) are aggregated over the entire evaluation dataset,
# not per batch. For per-batch logging, use `on_prediction_step`.

# Save and push to Hub
trainer.save_model(training_args.output_dir)
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