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61 lines
3.5 KiB
Markdown
61 lines
3.5 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# BARThez
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## Overview
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BARThez モデルは、Moussa Kamal Eddine、Antoine J.-P によって [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://huggingface.co/papers/2010.12321) で提案されました。ティクシエ、ミカリス・ヴァジルジャンニス、10月23日、
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2020年。
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論文の要約:
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*帰納的転移学習は、自己教師あり学習によって可能になり、自然言語処理全体を実行します。
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(NLP) 分野は、BERT や BART などのモデルにより、無数の自然言語に新たな最先端技術を確立し、嵐を巻き起こしています。
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タスクを理解すること。いくつかの注目すべき例外はありますが、利用可能なモデルと研究のほとんどは、
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英語を対象に実施されました。この作品では、フランス語用の最初の BART モデルである BARTez を紹介します。
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（我々の知る限りに）。 BARThez は、過去の研究から得た非常に大規模な単一言語フランス語コーパスで事前トレーニングされました
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BART の摂動スキームに合わせて調整しました。既存の BERT ベースのフランス語モデルとは異なり、
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CamemBERT と FlauBERT、BARThez は、エンコーダだけでなく、
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そのデコーダは事前トレーニングされています。 FLUE ベンチマークからの識別タスクに加えて、BARThez を新しい評価に基づいて評価します。
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この論文とともにリリースする要約データセット、OrangeSum。また、すでに行われている事前トレーニングも継続します。
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BARTHez のコーパス上で多言語 BART を事前訓練し、結果として得られるモデル (mBARTHez と呼ぶ) が次のことを示します。
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バニラの BARThez を大幅に強化し、CamemBERT や FlauBERT と同等かそれを上回ります。*
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このモデルは [moussakam](https://huggingface.co/moussakam) によって寄稿されました。著者のコードは[ここ](https://github.com/moussaKam/BARThez)にあります。
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<Tip>
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BARThez の実装は、トークン化を除いて BART と同じです。詳細については、[BART ドキュメント](bart) を参照してください。
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構成クラスとそのパラメータ。 BARThez 固有のトークナイザーについては以下に記載されています。
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</Tip>
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### Resources
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- BARThez は、BART と同様の方法でシーケンス間のタスクを微調整できます。以下を確認してください。
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[examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md)。
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## BarthezTokenizer
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[[autodoc]] BarthezTokenizer
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## BarthezTokenizerFast
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[[autodoc]] BarthezTokenizerFast
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