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Source code for transformers.training_args_seq2seq
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import logging
from dataclasses import dataclass, field
from typing import Optional
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).
generation_max_length (:obj:`int`, `optional`):
The :obj:`max_length` to use on each evaluation loop when :obj:`predict_with_generate=True`. Will default to
the :obj:`max_length` value of the model configuration.
generation_num_beams (:obj:`int`, `optional`):
The :obj:`num_beams` to use on each evaluation loop when :obj:`predict_with_generate=True`. Will default to the
:obj:`num_beams` value of the model configuration.
"""
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)."}
)
generation_max_length: Optional[int] = field(
default=None,
metadata={
"help": "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
"to the `max_length` value of the model configuration."
},
)
generation_num_beams: Optional[int] = field(
default=None,
metadata={
"help": "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
"to the `num_beams` value of the model configuration."
},
)