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46 lines
2.0 KiB
Python
46 lines
2.0 KiB
Python
import logging
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from dataclasses import dataclass, field
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from typing import Optional
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from seq2seq_trainer import arg_to_scheduler
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from transformers import TrainingArguments
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logger = logging.getLogger(__name__)
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@dataclass
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class Seq2SeqTrainingArguments(TrainingArguments):
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"""
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Parameters:
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label_smoothing (:obj:`float`, `optional`, defaults to 0):
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The label smoothing epsilon to apply (if not zero).
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sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to SortishSamler or not. It sorts the inputs according to lenghts in-order to minimizing the padding size.
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predict_with_generate (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to use generate to calculate generative metrics (ROUGE, BLEU).
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"""
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label_smoothing: Optional[float] = field(
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default=0.0, metadata={"help": "The label smoothing epsilon to apply (if not zero)."}
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)
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sortish_sampler: bool = field(default=False, metadata={"help": "Whether to SortishSamler or not."})
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predict_with_generate: bool = field(
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default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
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)
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adafactor: bool = field(default=False, metadata={"help": "whether to use adafactor"})
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encoder_layerdrop: Optional[float] = field(
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default=None, metadata={"help": "Encoder layer dropout probability. Goes into model.config."}
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)
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decoder_layerdrop: Optional[float] = field(
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default=None, metadata={"help": "Decoder layer dropout probability. Goes into model.config."}
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)
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dropout: Optional[float] = field(default=None, metadata={"help": "Dropout probability. Goes into model.config."})
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attention_dropout: Optional[float] = field(
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default=None, metadata={"help": "Attention dropout probability. Goes into model.config."}
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)
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lr_scheduler: Optional[str] = field(
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default="linear",
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metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys())}"},
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)
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