And with that, you're ready to start training! Training will take several hours. Depending on your GPU, 
it is possible that you will encounter a CUDA "out-of-memory" error when you start training. In this case, you can reduce 
the per_device_train_batch_size incrementally by factors of 2 and increase gradient_accumulation_steps by 2x to compensate.

trainer.train()

To be able to use your checkpoint with a pipeline, make sure to save the processor with the checkpoint: