文獻記錄－BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
摘要： bert 是用transformer的encoder 來構建的雙向預訓練模型，訓練過程是無監督的，並且可以通過fine-tune的方式去獲得較好的多個下游任務的效果． 簡介： 預訓練模型對於NLP的數據特徵的提取有着很大作用，爲了找到，句子和句子，詞語和詞語之間的聯繫． 現有的預訓練模型有兩種：基於特徵的(elmo)；微調(GPT) 特點： １：Bert使用了掩語預測的模型． ２：雙向 ３
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- 3. 《BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding》
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- 8. 論文翻譯：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 9. 譯文：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. BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding
- 5. Bert：論文閱讀-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 6. 論文閱讀筆記：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 7. 論文筆記《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
- 8. 論文翻譯：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 9. 譯文：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 10. 論文學習《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》