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

Autor:	Shnarch, Eyal, Halfon, Alon, Gera, Ariel, Danilevsky, Marina, Katsis, Yannis, Choshen, Leshem, Cooper, Martin Santillan, Epelboim, Dina, Zhang, Zheng, Wang, Dakuo, Yip, Lucy, Ein-Dor, Liat, Dankin, Lena, Shnayderman, Ilya, Aharonov, Ranit, Li, Yunyao, Liberman, Naftali, Slesarev, Philip Levin, Newton, Gwilym, Ofek-Koifman, Shila, Slonim, Noam, Katz, Yoav
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Human-Computer Interaction
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 2 figures To be published at EMNLP 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.01483 Zobrazit plný text záznamu View this record from Arxiv