def add_model_specific_args()

in kilt/readers/t5/base_transformer.py [0:0]

• 56 lines of code
• 1 McCabe index (conditional complexity)

    def add_model_specific_args(parser, root_dir):
        parser.add_argument(
            "--model_name_or_path",
            default=None,
            type=str,
            required=True,
            help="Path to pretrained model or model identifier from huggingface.co/models",
        )
        parser.add_argument(
            "--config_name",
            default="",
            type=str,
            help="Pretrained config name or path if not the same as model_name",
        )
        parser.add_argument(
            "--tokenizer_name",
            default="",
            type=str,
            help="Pretrained tokenizer name or path if not the same as model_name",
        )
        parser.add_argument(
            "--cache_dir",
            default="",
            type=str,
            help="Where do you want to store the pre-trained models downloaded from s3",
        )
        parser.add_argument(
            "--learning_rate",
            default=5e-5,
            type=float,
            help="The initial learning rate for Adam.",
        )
        parser.add_argument(
            "--weight_decay",
            default=0.0,
            type=float,
            help="Weight decay if we apply some.",
        )
        parser.add_argument(
            "--adam_epsilon",
            default=1e-8,
            type=float,
            help="Epsilon for Adam optimizer.",
        )
        parser.add_argument(
            "--warmup_steps",
            default=0,
            type=int,
            help="Linear warmup over warmup_steps.",
        )
        parser.add_argument(
            "--num_train_epochs",
            default=3,
            type=int,
            help="Total number of training epochs to perform.",
        )