def add_args(parser): """Add criterion-specific arguments to the parser.""" # fmt: off LabelSmoothedCrossEntropyCriterion.add_args(parser) parser.add_argument('--print-training-sample-interval', type=int, metavar='N', dest='print_interval', default=500, help='print a training sample (reference + ' 'prediction) every this number of updates') parser.add_argument('--smoothing-type', type=str, default='uniform', choices=['uniform', 'unigram', 'temporal'], help='label smoothing type. Default: uniform') parser.add_argument('--unigram-pseudo-count', type=float, default=1.0, metavar='C', help='pseudo count for unigram label ' 'smoothing. Only relevant if --smoothing-type=unigram')
def add_args(parser): """Add criterion-specific arguments to the parser.""" # fmt: off LabelSmoothedCrossEntropyCriterion.add_args(parser) parser.add_argument('--edit-samples-path', type=str, metavar='D', help='path to training edits tsv') parser.add_argument('--stability-coeff', default=1e2, type=float, metavar='D', help='Stability loss multiplier') parser.add_argument('--editability-coeff', default=1e2, type=float, metavar='D', help='Failed edit penalty multiplier') parser.add_argument('--edit-max-steps', default=10, type=int, metavar='D', help='Max steps to perform during an editing') parser.add_argument('--edit-learning-rate', default=1e-3, type=float, metavar='D', help='Learning rate for RMSPror editor') parser.add_argument('--almost-last', default=0, type=int, metavar='D', help='if 0 use the last decoder layer to perform an edit else use penultimate')
def add_args(parser): """Add criterion-specific arguments to the parser.""" # fmt: off LabelSmoothedCrossEntropyCriterion.add_args(parser) parser.add_argument("--print-training-sample-interval", type=int, metavar="N", dest="print_interval", default=500, help="print a training sample (reference + " "prediction) every this number of updates") parser.add_argument("--smoothing-type", type=str, default="uniform", choices=["uniform", "unigram", "temporal"], help="label smoothing type. Default: uniform") parser.add_argument( "--unigram-pseudo-count", type=float, default=1.0, metavar="C", help="pseudo count for unigram label " "smoothing. Only relevant if --smoothing-type=unigram")
def add_args(parser): """Add criterion-specific arguments to the parser.""" # fmt: off LabelSmoothedCrossEntropyCriterion.add_args(parser) parser.add_argument('--print-training-sample-interval', type=int, metavar='N', dest='print_interval', default=500, help='print a training sample (reference + ' 'prediction) every this number of updates') parser.add_argument('--smoothing-type', type=str, default='uniform', choices=['uniform', 'unigram', 'temporal'], help='label smoothing type. Default: uniform') parser.add_argument( '--unigram-pseudo-count', type=float, default=1.0, metavar='C', help='pseudo count for unigram label ' 'smoothing. Only relevant if --smoothing-type=unigram') parser.add_argument( '--scheduled-sampling-probs', type=lambda p: eval_str_list(p), metavar='P_1,P_2,...,P_N', default=1.0, help='scheduled sampling probabilities of sampling the truth ' 'labels for N epochs starting from --start-schedule-sampling-epoch; ' 'all later epochs using P_N') parser.add_argument( '--start-scheduled-sampling-epoch', type=int, metavar='N', default=1, help='start scheduled sampling from the specified epoch')