pytorch_translate/char_encoder.py [34:86]:
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    )
    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"),
    )
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pytorch_translate/char_source_transformer_model.py [43:95]:
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        )
        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"),
        )
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