2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license parameters labelsmoothing obj float optional defaults to 0 the label smoothing epsilon to apply if not zero sortishsampler obj bool optional defaults to obj false whether to sortishsamler or not it sorts the inputs according to lengths inorder to minimizing the padding size predictwithgenerate obj bool optional defaults to obj false whether to use generate to calculate generative metrics rouge bleu 2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license parameters label_smoothing obj float optional defaults to 0 the label smoothing epsilon to apply if not zero sortish_sampler obj bool optional defaults to obj false whether to sortishsamler or not it sorts the inputs according to lengths in order to minimizing the padding size predict_with_generate obj bool optional defaults to obj false whether to use generate to calculate generative metrics rouge bleu
import logging
from dataclasses import dataclass, field
from typing import Optional

from seq2seq_trainer import arg_to_scheduler

from transformers import TrainingArguments


logger = logging.getLogger(__name__)


@dataclass
class Seq2SeqTrainingArguments(TrainingArguments):
    

    label_smoothing: Optional[float] = field(
        default=0.0, metadata={"help": "The label smoothing epsilon to apply (if not zero)."}
    )
    sortish_sampler: bool = field(default=False, metadata={"help": "Whether to SortishSamler or not."})
    predict_with_generate: bool = field(
        default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
    )
    adafactor: bool = field(default=False, metadata={"help": "whether to use adafactor"})
    encoder_layerdrop: Optional[float] = field(
        default=None, metadata={"help": "Encoder layer dropout probability. Goes into model.config."}
    )
    decoder_layerdrop: Optional[float] = field(
        default=None, metadata={"help": "Decoder layer dropout probability. Goes into model.config."}
    )
    dropout: Optional[float] = field(default=None, metadata={"help": "Dropout probability. Goes into model.config."})
    attention_dropout: Optional[float] = field(
        default=None, metadata={"help": "Attention dropout probability. Goes into model.config."}
    )
    lr_scheduler: Optional[str] = field(
        default="linear",
        metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys())}"},
    )