def add_args()

in pytorch_translate/char_encoder.py [0:0]

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

def add_args(parser):
    parser.add_argument(
        "--char-embed-dim",
        type=int,
        default=128,
        metavar="N",
        help=("Character embedding dimension."),
    )
    parser.add_argument(
        "--char-rnn-units",
        type=int,
        default=256,
        metavar="N",
        help=("Number of units for Character LSTM."),
    )
    parser.add_argument(
        "--char-rnn-layers",
        type=int,
        default=1,
        metavar="N",
        help=("Number of Character LSTM layers."),
    )
    parser.add_argument(
        "--char-cnn-params",
        type=str,
        metavar="EXPR",
        help=("String experission, [(dim, kernel_size), ...]."),
    )
    parser.add_argument(
        "--char-cnn-nonlinear-fn",
        type=str,
        default="tanh",
        metavar="EXPR",
        help=("Nonlinearity applied to char conv outputs. Values: relu, tanh."),
    )
    parser.add_argument(
        "--char-cnn-num-highway-layers",
        type=int,
        default=0,
        metavar="N",
        help=("Char cnn encoder highway layers."),
    )
    parser.add_argument(
        "--char-cnn-output-dim",
        type=int,
        default=-1,
        metavar="N",
        help="Output dim of the CNN layer. If set to -1, this is computed "
        "from char-cnn-params.",
    )
    parser.add_argument(
        "--use-pretrained-weights",
        type=utils.bool_flag,
        nargs="?",
        const=True,
        default=False,
        help="Use pretrained weights for the character model including "
        "the char embeddings, CNN filters, highway networks",
    )
    parser.add_argument(
        "--finetune-pretrained-weights",
        type=utils.bool_flag,
        nargs="?",
        const=True,
        default=False,
        help="Boolean flag to specify whether or not to update the "
        "pretrained weights as part of training",
    )
    parser.add_argument(
        "--pretrained-weights-file",
        type=str,
        default="",
        help=("Weights file for loading pretrained weights"),
    )
    parser.add_argument(
        "--unk-only-char-encoding",
        type=utils.bool_flag,
        nargs="?",
        const=True,
        default=False,
        help=(
            "Boolean flag. When True, taking words embeddings"
            "for in-vocab tokens and char encoder's outputs for oov tokens"
            "When False, concatenating words embeddings and char encoder's outputs"
            "for all tokens."
        ),
    )