Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
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.
Keywords
Cite
@article{arxiv.2208.01483,
title = {Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours},
author = {Eyal Shnarch and Alon Halfon and Ariel Gera and Marina Danilevsky and Yannis Katsis and Leshem Choshen and Martin Santillan Cooper and Dina Epelboim and Zheng Zhang and Dakuo Wang and Lucy Yip and Liat Ein-Dor and Lena Dankin and Ilya Shnayderman and Ranit Aharonov and Yunyao Li and Naftali Liberman and Philip Levin Slesarev and Gwilym Newton and Shila Ofek-Koifman and Noam Slonim and Yoav Katz},
journal= {arXiv preprint arXiv:2208.01483},
year = {2022}
}
Comments
7 pages, 2 figures To be published at EMNLP 2022