Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
This addresses the barrier for non-experts in utilizing NLP for text classification, making it more accessible and efficient.
The paper tackles the problem of building custom text classifiers without coding or ML expertise by introducing Label Sleuth, a no-code open-source system that enables users to create a classifier from unlabeled text in a few hours.
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.