490b39e614
* Add label smoothing in Trainer * Add options for scheduler and Adafactor in Trainer * Put Seq2SeqTrainer in the main lib * Apply suggestions from code review Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Address review comments and adapt scripts * Documentation * Move test not using script to tests folder Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
43 lines
1.7 KiB
Python
43 lines
1.7 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from dataclasses import dataclass, field
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from .file_utils import add_start_docstrings
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from .training_args import TrainingArguments
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logger = logging.getLogger(__name__)
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@dataclass
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@add_start_docstrings(TrainingArguments.__doc__)
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class Seq2SeqTrainingArguments(TrainingArguments):
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"""
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sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to use a `sortish sampler` or not. Only possible if the underlying datasets are `Seq2SeqDataset` for
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now but will become generally available in the near future.
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It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for
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the training set.
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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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sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler 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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