# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from fairseq import options def get_reranking_parser(default_task='translation'): parser = options.get_parser('Generation and reranking', default_task) add_reranking_args(parser) return parser def get_tuning_parser(default_task='translation'): parser = options.get_parser('Reranking tuning', default_task) add_reranking_args(parser) add_tuning_args(parser) return parser def add_reranking_args(parser): group = parser.add_argument_group("Reranking") # fmt: off group.add_argument('--score-model1', '-s1', type=str, metavar='FILE', required=True, help='path to first model or ensemble of models for rescoring') group.add_argument('--score-model2', '-s2', type=str, metavar='FILE', required=False, help='path to second model or ensemble of models for rescoring') group.add_argument('--num-rescore', '-n', type=int, metavar='N', default=10, help='the number of candidate hypothesis to rescore') group.add_argument('-bz', '--batch-size', type=int, metavar='N', default=128, help='batch size for generating the nbest list') group.add_argument('--gen-subset', default='test', metavar='SET', choices=['test', 'train', 'valid'], help='data subset to generate (train, valid, test)') group.add_argument('--gen-model', default=None, metavar='FILE', help='the model to generate translations') group.add_argument('-b1', '--backwards1', action='store_true', help='whether or not the first model group is backwards') group.add_argument('-b2', '--backwards2', action='store_true', help='whether or not the second model group is backwards') group.add_argument('-a', '--weight1', default=1, nargs='+', type=float, help='the weight(s) of the first model') group.add_argument('-b', '--weight2', default=1, nargs='+', type=float, help='the weight(s) of the second model, or the gen model if using nbest from interactive.py') group.add_argument('-c', '--weight3', default=1, nargs='+', type=float, help='the weight(s) of the third model') # lm arguments group.add_argument('-lm', '--language-model', default=None, metavar='FILE', help='language model for target language to rescore translations') group.add_argument('--lm-dict', default=None, metavar='FILE', help='the dict of the language model for the target language') group.add_argument('--lm-name', default=None, help='the name of the language model for the target language') group.add_argument('--lm-bpe-code', default=None, metavar='FILE', help='the bpe code for the language model for the target language') group.add_argument('--data-dir-name', default=None, help='name of data directory') group.add_argument('--lenpen', default=1, nargs='+', type=float, help='length penalty: <1.0 favors shorter, >1.0 favors longer sentences') group.add_argument('--score-dict-dir', default=None, help='the directory with dictionaries for the scoring models') group.add_argument('--right-to-left1', action='store_true', help='whether the first model group is a right to left model') group.add_argument('--right-to-left2', action='store_true', help='whether the second model group is a right to left model') group.add_argument('--remove-bpe', default='@@ ', help='the bpe symbol, used for the bitext and LM') group.add_argument('--prefix-len', default=None, type=int, help='the length of the target prefix to use in rescoring (in terms of words wo bpe)') group.add_argument('--sampling', action='store_true', help='use sampling instead of beam search for generating n best list') group.add_argument('--diff-bpe', action='store_true', help='bpe for rescoring and nbest list not the same') group.add_argument('--rescore-bpe-code', default=None, help='bpe code for rescoring models') group.add_argument('--nbest-list', default=None, help='use predefined nbest list in interactive.py format') group.add_argument('--write-hypos', default=None, help='filename prefix to write hypos to') group.add_argument('--ref-translation', default=None, help='reference translation to use with nbest list from interactive.py') group.add_argument('--backwards-score-dict-dir', default=None, help='the directory with dictionaries for the backwards model,' 'if None then it is assumed the fw and backwards models share dictionaries') # extra scaling args group.add_argument('--gen-model-name', default=None, help='the name of the models that generated the nbest list') group.add_argument('--model1-name', default=None, help='the name of the set for model1 group ') group.add_argument('--model2-name', default=None, help='the name of the set for model2 group') group.add_argument('--shard-id', default=0, type=int, help='the id of the shard to generate') group.add_argument('--num-shards', default=1, type=int, help='the number of shards to generate across') group.add_argument('--all-shards', action='store_true', help='use all shards') group.add_argument('--target-prefix-frac', default=None, type=float, help='the fraction of the target prefix to use in rescoring (in terms of words wo bpe)') group.add_argument('--source-prefix-frac', default=None, type=float, help='the fraction of the source prefix to use in rescoring (in terms of words wo bpe)') group.add_argument('--normalize', action='store_true', help='whether to normalize by src and target len') return group def add_tuning_args(parser): group = parser.add_argument_group("Tuning") group.add_argument('--lower-bound', default=[-0.7], nargs='+', type=float, help='lower bound of search space') group.add_argument('--upper-bound', default=[3], nargs='+', type=float, help='upper bound of search space') group.add_argument('--tune-param', default=['lenpen'], nargs='+', choices=['lenpen', 'weight1', 'weight2', 'weight3'], help='the parameter(s) to tune') group.add_argument('--tune-subset', default='valid', choices=['valid', 'test', 'train'], help='the subset to tune on ') group.add_argument('--num-trials', default=1000, type=int, help='number of trials to do for random search') group.add_argument('--share-weights', action='store_true', help='share weight2 and weight 3') return group