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55 lines
3.1 KiB
Markdown
55 lines
3.1 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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# CPM
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## Overview
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CPM モデルは、Zhengyan Zhang、Xu Han、Hao Zhou、Pei Ke、Yuxian Gu によって [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://huggingface.co/papers/2012.00413) で提案されました。葉徳明、秦裕佳、
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Yusheng Su、Haozhe Ji、Jian Guan、Fanchao Qi、Xiaozi Wang、Yanan Zheng、Guoyang Zeng、Huanqi Cao、Shengqi Chen、
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Daixuan Li、Zhenbo Sun、Zhiyuan Liu、Minlie Huang、Wentao Han、Jie Tang、Juanzi Li、Xiaoyan Zhu、Maosong Sun。
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論文の要約は次のとおりです。
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*事前トレーニングされた言語モデル (PLM) は、さまざまな下流の NLP タスクに有益であることが証明されています。最近ではGPT-3、
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1,750億個のパラメータと570GBの学習データを備え、数回の撮影（1枚でも）の容量で大きな注目を集めました
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ゼロショット）学習。ただし、GPT-3 を適用して中国語の NLP タスクに対処することは依然として困難です。
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GPT-3 の言語は主に英語であり、パラメーターは公開されていません。この技術レポートでは、
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大規模な中国語トレーニング データに対する生成的事前トレーニングを備えた中国語事前トレーニング済み言語モデル (CPM)。最高に
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私たちの知識の限りでは、26 億のパラメータと 100GB の中国語トレーニング データを備えた CPM は、事前トレーニングされた中国語としては最大のものです。
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言語モデルは、会話、エッセイの作成、
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クローゼテストと言語理解。広範な実験により、CPM が多くの環境で優れたパフォーマンスを達成できることが実証されています。
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少数ショット (ゼロショットでも) 学習の設定での NLP タスク。*
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このモデルは [canwenxu](https://huggingface.co/canwenxu) によって提供されました。オリジナルの実装が見つかります
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ここ: https://github.com/TsinghuaAI/CPM-Generate
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<Tip>
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CPM のアーキテクチャは、トークン化方法を除いて GPT-2 と同じです。詳細については、[GPT-2 ドキュメント](openai-community/gpt2) を参照してください。
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API リファレンス情報。
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</Tip>
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## CpmTokenizer
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[[autodoc]] CpmTokenizer
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## CpmTokenizerFast
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[[autodoc]] CpmTokenizerFast
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