[TrainingArguments] you are using. Those will go in subfolder named checkpoint-xxx with xxx
being the step at which the training was at.
Resuming training from a checkpoint can be done when calling [Trainer.train] with either:
resume_from_checkpoint=True which will resume training from the latest checkpoint
resume_from_checkpoint=checkpoint_dir which will resume training from the specific checkpoint in the directory
  passed.
In addition, you can easily save your checkpoints on the Model Hub when using push_to_hub=True. By default, all
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
the hub-strategy value of your [TrainingArguments] to either:
"checkpoint": the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
