examples/research_projects/seq2seq-distillation/finetune.py/SummarizationModule/add_model_specific_args
class SummarizationModule:    def add_model_specific_args(parser, root_dir):
        BaseTransformer.add_model_specific_args(parser, root_dir)
        add_generic_args(parser, root_dir)
        parser.add_argument(
            "--max_source_length",
            default=1024,
            type=int,
            help=(
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            ),
        )
        parser.add_argument(
            "--max_target_length",
            default=56,
            type=int,
            help=(
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            ),
        )
        parser.add_argument(
            "--val_max_target_length",
            default=142,  # these defaults are optimized for CNNDM. For xsum, see README.md.
            type=int,
            help=(
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            ),
        )
        parser.add_argument(
            "--test_max_target_length",
            default=142,
            type=int,
            help=(
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            ),
        )
        parser.add_argument("--freeze_encoder", action="store_true")
        parser.add_argument("--freeze_embeds", action="store_true")
        parser.add_argument("--sortish_sampler", action="store_true", default=False)
        parser.add_argument("--overwrite_output_dir", action="store_true", default=False)
        parser.add_argument("--max_tokens_per_batch", type=int, default=None)
        parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
        parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
        parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
        parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
        parser.add_argument(
            "--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
        )
        parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
        parser.add_argument("--src_lang", type=str, default="", required=False)
        parser.add_argument("--tgt_lang", type=str, default="", required=False)
        parser.add_argument("--eval_beams", type=int, default=None, required=False)
        parser.add_argument(
            "--val_metric", type=str, default=None, required=False, choices=["bleu", "rouge2", "loss", None]
        )
        parser.add_argument("--eval_max_gen_length", type=int, default=None, help="never generate more than n tokens")
        parser.add_argument("--save_top_k", type=int, default=1, required=False, help="How many checkpoints to save")
        parser.add_argument(
            "--early_stopping_patience",
            type=int,
            default=-1,
            required=False,
            help=(
                "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
                " val_check_interval will effect it."
            ),
        )
        return parser