論文分享 -- > NLP -- > Language Models are Unsupervised Multitask Learners
本次要總結和分享的論文是GPT2，參考的實現代碼model。本論文方法是在openAI-GPT的基礎上進行了一些微小的修改獲得的，從模型的角度來說，只是大幅增大了模型規模，去掉了fine-tune過程，不管是在預訓練和預測階段都是徹底的無監督，這點就很難以想象，可是的確作到了，並且效果還不錯。網上對這篇論文的講解很是多，這裏本人就講下本身淺薄的看法。html 動機和創新點 傳統機器學習任務只是拿某
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- 8. 論文分享-->Attention is all you need
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相關文章
- 1. GPT2--Language Models are Unsupervised Multitask Learners
- 2. GPT2.0 Language Models are Unsupervised Multitask Learners 論文解讀
- 3. GPT-3：Language Models are Few-Shot Learners 論文解讀
- 4. OpenAI GPT-2語言模型是非監督多任務學習器 Language Models are Unsupervised Multitask Learners
- 5. 論文分享 -- > NLP -- > Neural machine Translation of Rare Words with Subword Units
- 6. Big Self-Supervised Models are Strong Semi-Supervised Learners論文研讀
- 7. 深度學習 -- > NLP -- >Improving Language Understanding by Generative Pre-Training
- 8. 論文分享-->Attention is all you need
- 9. 論文閱讀 decaNLP -- The Natural Language Decathlon: Multitask Leaning as Question Answering
- 10. 深度學習 -- > NLP-- > BERT