译文：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

时间 2020-12-30

标签译文算法自然语言处理繁體版

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Bert: 针对语言理解双向深度transformer的预训练模型摘要本文介绍一种新的语言表达模型-BERT(Bidirectional Encoder Representations from Transformers).与近期语言表达模型 (Peters et al., 2018a; Radford et al., 2018)不同的是，Bert通过在所有层上调节双向上下文来预训练未标定数据

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1. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
3. 《BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding》
4. BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding
5. 论文翻译：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
6. Bert：论文阅读-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
7. 论文阅读笔记：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
8. 文献记录－BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
9. 论文笔记《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
10. 论文学习《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
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1. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
3. 《BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding》
4. BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding
5. 论文翻译：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
6. Bert：论文阅读-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
7. 论文阅读笔记：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
8. 文献记录－BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
9. 论文笔记《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
10. 论文学习《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》

>>更多相关文章<<