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')
