SMART: An Open Source Data Labeling Platform for Supervised Learning
This provides a tool for data scientists and research teams to streamline data labeling, but it is incremental as it builds on existing labeling and active learning concepts.
The authors tackled the problem of efficiently building labeled training datasets for supervised machine learning by developing SMART, an open source web application that includes an intuitive interface, active learning to reduce labeling effort, and inter-rater reliability statistics for quality insight.
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.