Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours

Eyal Shnarch; Alon Halfon; Ariel Gera; Marina Danilevsky; Yannis Katsis; Leshem Choshen; Martin Santillan Cooper; Dina Epelboim; Zheng Zhang; Dakuo Wang; Lucy Yip; Liat Ein-Dor; Lena Dankin; Ilya Shnayderman; Ranit Aharonov; Yunyao Li; Naftali Liberman; Philip Levin Slesarev; Gwilym Newton; Shila Ofek-Koifman; Noam Slonim; Yoav Katz

arXiv · 2208.01483

Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours

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Abstract

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.

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Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, Yoav Katz. 2022-08-02. Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours. https://arxiv.org/abs/2208.01483

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