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)