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R11484/source_code/transformers/examples/legacy/seq2seq/seq2seq_training_args.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.
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())}"},
)
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