mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
1c8c2d9ab3
* move old s2s scripts to legacy * add the tests back * proper rename * restore * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Stas Bekman <stas@stason.org> Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
60 lines
2.6 KiB
Python
60 lines
2.6 KiB
Python
# 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())}"},
|
|
)
|