论文标题
标签侦探：从未标记的文本到几个小时后的分类器
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
论文作者
论文摘要
文本分类在许多现实世界中可能很有用，为最终用户节省了很多时间。但是，建立自定义分类器通常需要编码技能和ML知识，这对许多潜在用户构成了重大障碍。为了提高此障碍，我们介绍了标签侦探，这是一种免费的开源系统，用于标记和创建文本分类器。该系统对于（a）是一个无代码系统是独一无二的，它使得非专家可以访问NLP，（b）指导用户通过整个标签过程，直到获得自定义分类器，从而使过程有效 - 从冷启动到几个小时内的分类器，并且（c）开发人员配置和扩展。通过开放采购标签侦探，我们希望建立一个将扩大NLP模型利用率的用户和开发人员社区。
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML knowledge, which poses a significant barrier for many potential users. To lift this barrier, we introduce Label Sleuth, a free open source system for labeling and creating text classifiers. This system is unique for (a) being a no-code system, making NLP accessible to non-experts, (b) guiding users through the entire labeling process until they obtain a custom classifier, making the process efficient -- from cold start to classifier in a few hours, and (c) being open for configuration and extension by developers. By open sourcing Label Sleuth we hope to build a community of users and developers that will broaden the utilization of NLP models.
