论文题目: ERNIE: Enhanced Language Representation with Informative Entities(THU/ACL2019) 本文的工作也是属于对BERT锦上添花,将知识图谱的一些结构化信息融入到BERT中,使其更好地对真实世界进行语义建模.也就是说,原始的bert模型只是机械化地去学习语言相关的“合理性”,而并学习不到语言之间的语义联系,打个比喻,就比如掉包xia只会掉包,而不懂每个包里面具体是什么含义.于是,作者们的工作就是如何将这些额外的知识告诉
https://github.com/google-research/bert BERT ***** New May 31st, 2019: Whole Word Masking Models ***** This is a release of several new models which were the result of an improvement the pre-processing code. In the original pre-processing code, we ra
用NVIDIA-NGC对BERT进行训练和微调 Training and Fine-tuning BERT Using NVIDIA NGC 想象一下一个比人类更能理解语言的人工智能程序.想象一下为定制的域或应用程序构建自己的Siri或Google搜索. Google BERT(来自Transformers的双向编码器表示)为自然语言处理(NLP)领域提供了一个改变游戏规则的转折点. BERT运行在NVIDIA GPUs驱动的超级计算机上,训练其庞大的神经网络,达到前所未有的NLP精度,冲击了已
As I walked through the large poster-filled hall at CVPR 2013, I asked myself, “Quo vadis Computer Vision?" (Where are you going, computer vision?) I see lots of papers which exploit last year’s ideas, copious amounts of incremental research, and an
Graph-powered Machine Learning at Google Thursday, October 06, 2016 Posted by Sujith Ravi, Staff Research Scientist, Google ResearchRecently, there have been significant advances in Machine Learning that enable computer systems to solve compl