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* Reorganize example folder * Continue reorganization * Change requirements for tests * Final cleanup * Finish regroup with tests all passing * Copyright * Requirements and readme * Make a full link for the documentation * Address review comments * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Add symlink * Reorg again * Apply suggestions from code review Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com> * Adapt title * Update to new strucutre * Remove test * Update READMEs Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
60 lines
2.6 KiB
Python
60 lines
2.6 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 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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