def add_training_args()

in blink/common/params.py [0:0]

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

    def add_training_args(self, args=None):
        """
        Add model training args.
        """
        parser = self.add_argument_group("Model Training Arguments")
        parser.add_argument(
            "--evaluate", action="store_true", help="Whether to run evaluation."
        )
        parser.add_argument(
            "--output_eval_file",
            default=None,
            type=str,
            help="The txt file where the the evaluation results will be written.",
        )
        parser.add_argument(
            "--train_batch_size", default=8, type=int, 
            help="Total batch size for training."
        )
        parser.add_argument("--max_grad_norm", default=1.0, type=float)
        parser.add_argument(
            "--learning_rate",
            default=3e-5,
            type=float,
            help="The initial learning rate for Adam.",
        )
        parser.add_argument(
            "--num_train_epochs",
            default=1,
            type=int,
            help="Number of training epochs.",
        )
        parser.add_argument(
            "--print_interval", type=int, default=10, 
            help="Interval of loss printing",
        )
        parser.add_argument(
           "--eval_interval",
            type=int,
            default=100,
            help="Interval for evaluation during training",
        )
        parser.add_argument(
            "--save_interval", type=int, default=1, 
            help="Interval for model saving"
        )
        parser.add_argument(
            "--warmup_proportion",
            default=0.1,
            type=float,
            help="Proportion of training to perform linear learning rate warmup for. "
            "E.g., 0.1 = 10% of training.",
        )
        parser.add_argument(
            "--gradient_accumulation_steps",
            type=int,
            default=1,
            help="Number of updates steps to accumualte before performing a backward/update pass.",
        )
        parser.add_argument(
            "--type_optimization",
            type=str,
            default="all_encoder_layers",
            help="Which type of layers to optimize in BERT",
        )
        parser.add_argument(
            "--shuffle", type=bool, default=False, 
            help="Whether to shuffle train data",
        )