class InferConfig(FairseqDataclass): task: Any = None decoding: DecodingConfig = DecodingConfig() common: CommonConfig = CommonConfig() common_eval: CommonEvalConfig = CommonEvalConfig() checkpoint: CheckpointConfig = CheckpointConfig() generation: GenerationConfig = GenerationConfig() distributed_training: DistributedTrainingConfig = DistributedTrainingConfig( ) dataset: DatasetConfig = DatasetConfig()
def add_dataset_args(parser, train=False, gen=False): group = parser.add_argument_group("dataset_data_loading") gen_parser_from_dataclass(group, DatasetConfig()) group.add_argument( '--aistrigh', nargs='+', metavar='LIST', help="[Path to data folder, language, window-length] if you wish " "to use AistrighNLP to reapply Celtic mutations on a demutated model") # fmt: on return group
class InferConfig(FairseqDataclass): task: Any = None decoding: DecodingConfig = DecodingConfig() common: CommonConfig = CommonConfig() common_eval: CommonEvalConfig = CommonEvalConfig() checkpoint: CheckpointConfig = CheckpointConfig() distributed_training: DistributedTrainingConfig = DistributedTrainingConfig() dataset: DatasetConfig = DatasetConfig() is_ax: bool = field( default=False, metadata={ "help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume" }, )
def add_dataset_args(parser, train=False, gen=False): group = parser.add_argument_group("dataset_data_loading") gen_parser_from_dataclass(group, DatasetConfig()) # fmt: on return group