def METHOD_NAME(self, output_dir: str):
    """
    We create a SetFitModel object from a pretrained model, then create a SetFitTrainer object with
    the model, and then train the model
    """
    from span_marker import Trainer
    from transformers import TrainingArguments
    self.trainer_kwargs["output_dir"] = output_dir
    # prepare data
    self.init_model()
    # set trainer
    self._training_args = TrainingArguments(**self.trainer_kwargs)
    self._span_marker_trainer = Trainer(
        args=self._training_args,
        model=self._span_marker_model,
        train_dataset=self._train_dataset,
        eval_dataset=self._eval_dataset,
    )
    # train
    self._span_marker_trainer.METHOD_NAME()
    if self._eval_dataset:
        self._metrics = self._span_marker_trainer.evaluate()
        self._logger.info(self._metrics)
    else:
        self._metrics = None
    self.save(output_dir)