The [Trainer] class provides an API for feature-complete training in PyTorch for most standard use cases. It's used in most of the example scripts.
Before instantiating your [Trainer], create a [TrainingArguments] to access all the points of customization during training.
The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch.
The [Trainer] contains the basic training loop which supports the above features. To inject custom behavior you can subclass them and override the following methods:
get_train_dataloader -- Creates the training DataLoader.
get_eval_dataloader -- Creates the evaluation DataLoader.
get_test_dataloader -- Creates the test DataLoader.
log -- Logs information on the various objects watching training.
create_optimizer_and_scheduler -- Sets up the optimizer and learning rate scheduler if they were not passed at
