Enable javascript in your browser for better experience. Need to know to enable it? Go here.
Last updated : May 19, 2020
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
May 2020
试验 ? 值得一试。了解为何要构建这一能力是很重要的。企业应当在风险可控的前提下在项目中尝试应用此项技术。

该技术之前处于技术雷达的评估维度。NLP(Natural Language Processing,自然语言处理领域的创新在持续快速发展,并且由于无处不在的 迁移学习 ,使得我们可以将这些创新应用到项目中。GLUE基准测试(一套语言理解任务)的得分在过去几年里有了显著的进步,平均分数从刚发布时的70.0上升到2020年4月处于领导地位的90.0。我们在NLP领域的很多项目,从ELMo、BERTERNIE等预训练模型开始,然后根据项目需求进行微调,可以取得重大进展。

Apr 2019
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

Transfer learning has been quite effective within the field of computer vision, speeding the time to train a model by reusing existing models. Those of us who work in machine learning are excited that the same techniques can be applied to natural language processing (NLP) with the publication of ULMFiT and open source pretrained models and code examples. We think transfer learning for NLP will significantly reduce the effort to create systems dealing with text classification.

已发布 : Apr 24, 2019


English | Español | Português | 中文