src/mrc_client/seq2seq/seq2seq_training_args.py

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):
    """
    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 lenghts 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).
    """

    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())}"},
    )