Hi there, here are the answers:


No, by default the model checkpointing only saves model to resume training later if something goes wrong, but there is no best model loading logic unless you use load_best_model_at_end. You will then need to set an eval_strategy and a save_strategy that match (either epoch or steps)


It’s the accumulated training loss since the beginning of the training.


No there is not inside Trainer. We integrate with most reporting tooling, TensorBoard, Wandb, CometML etc for this reason.


For your last questions, setting metric_for_best_model is not enough, you need to set load_best_model_at_end to True. The losses dispalyed in the trainer_state.json will still be the training losses.