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Source code for transformers.training_args_seq2seq
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# http://www.apache.org/licenses/LICENSE-2.0
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import logging
from dataclasses import dataclass, field
from .file_utils import add_start_docstrings
from .training_args import TrainingArguments
logger = logging.getLogger(__name__)
[docs]@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class Seq2SeqTrainingArguments(TrainingArguments):
"""
sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use a `sortish sampler` or not. Only possible if the underlying datasets are `Seq2SeqDataset` for
now but will become generally available in the near future.
It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for
the training set.
predict_with_generate (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
"""
sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)