Dina Epelboim


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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
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Label Sleuth is an open source platform for building text classifiers which does not require coding skills nor machine learning knowledge.- Project website: [https://www.label-sleuth.org/](https://www.label-sleuth.org/)- Link to screencast video: [https://vimeo.com/735675461](https://vimeo.com/735675461)### AbstractText classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a classifier generally 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: - being a no-code system, making NLP accessible for non-experts. - guiding its users throughout the entire labeling process until they obtain their desired classifier, making the process efficient - from cold start to a classifier in a few hours. - 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 widen the utilization of NLP models.