def get_args()

in anli/src/nli/evaluation.py [0:0]

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

def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--cpu", action="store_true", help="If set, we only use CPU.")
    parser.add_argument(
        "--model_class_name",
        type=str,
        help="Set the model class of the experiment.",
        required=True,
    )

    parser.add_argument(
        "--model_checkpoint_path",
        type=str,
        help="Set the path to save the prediction.",
        default="",
    )

    parser.add_argument(
        "--output_prediction_path",
        type=str,
        default=None,
        help="Set the path to save the prediction.",
    )

    parser.add_argument(
        "--per_gpu_eval_batch_size",
        default=16,
        type=int,
        help="Batch size per GPU/CPU for evaluation.",
    )

    parser.add_argument(
        "--max_length", default=156, type=int, help="Max length of the sequences."
    )

    parser.add_argument(
        "--eval_data", type=str, help="The training data used in the experiments."
    )

    parser.add_argument("--train_data", type=str, help="snli")

    parser.add_argument("--train_mode", type=str, help="orig")

    parser.add_argument(
        "--train_with_lm",
        default=False,
        action="store_true",
        help="Train model with LM",
    )

    parser.add_argument(
        "--flip_sent",
        default=False,
        action="store_true",
        help="Flip the hypothesis and premise",
    )

    parser.add_argument("--slurm", default=False, action="store_true")

    args = parser.parse_args()
    return args