【cs224n-12】Modeling contexts of use: Contextual Representations and Pretraining. ELMo and BERT.

时间 2021-01-02

标签自然语言处理繁體版

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上一篇我们讲解了词嵌入的静态表示和上下文动态表示的区别，即基于上下文动态表示的预训练模型的发展情况，本文是对具体细节的一些细化描述。 1. Representations for a word 我们可以从我们常见的词嵌入的静态表示模型 Word2vec, GloVe, fastText获得一个单词的表示，并应有与下游任务。 Tips for unknown words with

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1. Contextual Word Representations and Pretraining
2. cs224n学习4： Contextual Word Representations and Pretraining
3. NLP基础之——Contextual Word Representations and Pretraining
4. 【NLP CS224N笔记】Lecture 13 - Contextual Word Representations and Pretraining
5. Datawhale 零基础⼊⻔NLP-Task13 Contextual Word Representations and Pretraining
6. 【cs224n-11】Contextual Word Representations: BERT
7. 【论文阅读笔记】Linguistic Knowledge and Transferability of Contextual Representations
8. BERT and RoBERTa 知识点整理
9. Distributed Representations of Words and Phrases and their Compositionality
10. Distributed Representations of Sentences and Documents
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1. Contextual Word Representations and Pretraining
2. cs224n学习4： Contextual Word Representations and Pretraining
3. NLP基础之——Contextual Word Representations and Pretraining
4. 【NLP CS224N笔记】Lecture 13 - Contextual Word Representations and Pretraining
5. Datawhale 零基础⼊⻔NLP-Task13 Contextual Word Representations and Pretraining
6. 【cs224n-11】Contextual Word Representations: BERT
7. 【论文阅读笔记】Linguistic Knowledge and Transferability of Contextual Representations
8. BERT and RoBERTa 知识点整理
9. Distributed Representations of Words and Phrases and their Compositionality
10. Distributed Representations of Sentences and Documents

>>更多相关文章<<