Class TrainingArguments

src/transformers/src/transformers/training_args.py:215–3025

TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop itself**. Using [`HfArgumentParser`] we can turn this class into [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified

# TODO: `TrainingArguments` users rely on it being fully mutable. In the future see if we can narrow this to a few keys: https://github.com/huggingface/transformers/pull/25903
@dataclass
class TrainingArguments:
    """
    TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
    itself**.

    Using [`HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        output_dir (`str`):
            The output directory where the model predictions and checkpoints will be written.
        overwrite_output_dir (`bool`, *optional*, defaults to `False`):
            If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
            points to a checkpoint directory.
        do_train (`bool`, *optional*, defaults to `False`):
            Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
            by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_eval (`bool`, *optional*):
            Whether to run evaluation on the validation set or not. Will be set to `True` if `eval_strategy` is
            different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your
            training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_predict (`bool`, *optional*, defaults to `False`):
            Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
            intended to be used by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        eval_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
            The evaluation strategy to adopt during training. Possible values are:

                - `"no"`: No evaluation is done during training.
                - `"steps"`: Evaluation is done (and logged) every `eval_steps`.
                - `"epoch"`: Evaluation is done at the end of each epoch.

        prediction_loss_only (`bool`, *optional*, defaults to `False`):
            When performing evaluation and generating predictions, only returns the loss.
        per_device_train_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for training.
        per_device_eval_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for evaluation.
        gradient_accumulation_steps (`int`, *optional*, defaults to 1):
            Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

            <Tip warning={true}>

            When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
            evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples.

            </Tip>

        eval_accumulation_steps (`int`, *optional*):
            Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
            left unset, the whole predictions are accumulated on GPU/NPU/TPU before being moved to the CPU (faster but
            requires more memory).
        eval_delay (`float`, *optional*):
            Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
            eval_strategy.