def add_args(parser):
FairseqNATModel.add_args(parser)
# length prediction
parser.add_argument(
"--src-embedding-copy",
action="store_true",
help=
"copy encoder word embeddings as the initial input of the decoder",
)
parser.add_argument(
"--pred-length-offset",
action="store_true",
help=
"predicting the length difference between the target and source sentences",
)
parser.add_argument(
"--sg-length-pred",
action="store_true",
help="stop the gradients back-propagated from the length predictor",
)
parser.add_argument(
"--length-loss-factor",
type=float,
help="weights on the length prediction loss",
)
def add_args(parser):
FairseqNATModel.add_args(parser)
parser.add_argument(
"--early-exit",
default="6,6,6",
type=str,
help="number of decoder layers before word_del, mask_ins, word_ins",
)
parser.add_argument(
"--no-share-discriminator",
action="store_true",
help="separate parameters for discriminator",
)
parser.add_argument(
"--no-share-maskpredictor",
action="store_true",
help="separate parameters for mask-predictor",
)
parser.add_argument(
"--share-discriminator-maskpredictor",
action="store_true",
help=
"share the parameters for both mask-predictor and discriminator",
)
parser.add_argument(
"--sampling-for-deletion",
action="store_true",
help="instead of argmax, use sampling to predict the tokens",
)
def add_args(parser):
"""Add model-specific arguments to the parser."""
FairseqNATModel.add_args(parser)
# parser.add_argument('--encoder-learned-pos', action='store_true',
# help='use learned positional embeddings in the encoder')
parser.add_argument(
"--upsample-scale",
type=int,
help="upsampling scale s to use for encoder features")
def add_args(parser):
FairseqNATModel.add_args(parser)
parser.add_argument(
"--early-exit",
default="6,6,6",
type=str,
help="number of decoder layers before word_del, mask_ins, word_ins",
)
parser.add_argument(
"--no-share-discriminator",
action="store_true",
help="separate parameters for discriminator",
)
parser.add_argument(
"--no-share-maskpredictor",
action="store_true",
help="separate parameters for mask-predictor",
)
parser.add_argument(
"--share-discriminator-maskpredictor",
action="store_true",
help=
"share the parameters for both mask-predictor and discriminator",
)
parser.add_argument(
"--sampling-for-deletion",
action='store_true',
help='instead of argmax, use sampling to predict the tokens')
parser.add_argument(
"--cnn-normalize-after",
default=True,
type=bool,
help='weather using batch normalization after cnn module.')
parser.add_argument(
"--cnn-parameters-freeze",
default=True,
type=bool,
help='weather freeze the parameters of cnn modules.')
def add_args(parser):
FairseqNATModel.add_args(parser)
parser.add_argument("--label-tau", default=None, type=float)
