Text-analysis AI models have become part of everyday life, finishing your sentences, translating the web, and summarizing long passages of text. But adapting them to new tasks typically requires a domain expert to label new examples and a machine-learning expert to train the new model. "In the real world, you need to tweak and customize the out-of-the-box model," said Eyal Shnarch, an IBM researcher who specializes in natural language processing. "But there aren't enough machine-learning experts for everyone who wants a customized model." Label Sleuth is meant to change that.

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.