  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.
