et (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*):
             The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the
             `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each
             dataset prepending the dictionary key to the metric name.
        tokenizer ([`PreTrainedTokenizerBase`], *optional*):
            The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the
            maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
            interrupted training or reuse the fine-tuned model.
        model_init (`Callable[[], PreTrainedModel]`, *optional*):
            A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start
            from a new instance of the model as given by this function.

            The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
            be able to choose different architectures according to hyper parameters (such as layer count, sizes of
            inner layers, dropout probabilities etc).
        compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):
            The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return
            a dictionary string to metric values.
        callbacks (List of [`TrainerCallback`], *optional*):
            A list of callbacks to customize the training loop. Will add those to the list of default callbacks
            detailed in [here](callback).

            If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
        optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*): A tuple
            containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model
            and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
            A function that preprocess the logits right before caching them at each evaluation step. Must take two
            tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
            by this function will be reflected in the predictions received by `compute_metrics`.

            Note that the labels (second parameter) will be `None` if the dataset does not have them.

    Important attributes:

        - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]
          subclass.
        - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the
          original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`,
          the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner
          model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`.
        - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from
          data parallelism, this means some of the model layers are split on different GPUs).
        - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set
          to `False` if model parallel or deepspeed is used, or if the default
          `TrainingArguments.place_model_on_device` is overridden to return `False` .
        - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while
          in `train`)

    r