BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding
轉自 1.BERT模型 BERT的全稱是Bidirectional Encoder Representation from Transformers，即雙向Transformer的Encoder，因爲decoder是不能獲要預測的信息的。模型的主要創新點都在pre-train方法上，即用了Masked LM和Next Sentence Prediction兩種方法分別捕捉詞語和句子級別的repre
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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. 論文閱讀：BERT：Pre-training of Deep Bidirectional Transformers for Language Understanding
- 5. 論文閱讀筆記：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 6. Bert：論文閱讀-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 7. BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding 論文翻譯
- 8. 論文翻譯：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 9. 文獻記錄－BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 10. 論文筆記《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
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相關文章
- 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. 論文閱讀：BERT：Pre-training of Deep Bidirectional Transformers for Language Understanding
- 5. 論文閱讀筆記：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 6. Bert：論文閱讀-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 7. BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding 論文翻譯
- 8. 論文翻譯：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 9. 文獻記錄－BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 10. 論文筆記《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》