chunk_content: e=8,
      generation_max_length=128,
      logging_steps=25,
      remove_unused_columns=False,
      label_names=["labels"],
  )
  It is also a good idea to write a custom TrainerCallback to save model checkpoints during training:
  
  
  	Copied
  from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
  
  
  class SavePeftModelCallback(TrainerCallback):
      def on_save(
          self,
          args: TrainingArguments,
          state: TrainerState,
          control