SMART: An open source data labeling platform for supervised learning

Robert Franklin Chew; Peter Michael Baumgartner; Michael Frederick Wenger; Jason Matthew Nance; Dale Keith Richards; Emily Catherine Hadley; Caroline Patricia Kery; Rob Chew; Michael Frederick Wenger; Caroline Patricia Kery; Jason Matthew Nance; Keith Richards; Emily Catherine Hadley; Peter Michael Baumgartner

SMART

An open source data labeling platform for supervised learning

Chew, R., Wenger, M., Kery, C., Nance, J., Richards, K., Hadley, E., & Baumgartner, P. (2019). SMART: An open source data labeling platform for supervised learning. Journal of Machine Learning Research, 20(82), 1-5. Article 82. https://arxiv.org/abs/1812.06591

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Abstract

SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of labeled data, and incorporates inter-rater reliability statistics to provide insight into label quality. SMART is designed to be platform agnostic and easily deployable to meet the needs of as many different research teams as possible. The project website contains links to the code repository and extensive user documentation.