

[~Trainer.get_train_dataloader] creates a training DataLoader
[~Trainer.get_eval_dataloader] creates an evaluation DataLoader
[~Trainer.get_test_dataloader] creates a test DataLoader
[~Trainer.log] logs information on the various objects that watch training
[~Trainer.create_optimizer_and_scheduler] creates an optimizer and learning rate scheduler if they weren't passed in the __init__; these can also be separately customized with [~Trainer.create_optimizer] and [~Trainer.create_scheduler] respectively
[~Trainer.compute_loss] computes the loss on a batch of training inputs
[~Trainer.training_step] performs the training step
[~Trainer.prediction_step] performs the prediction and test step
[~Trainer.evaluate] evaluates the model and returns the evaluation metrics
[~Trainer.predict] makes predictions (with metrics if labels are available) on the test set