SMART: An Open Source Data Labeling Platform for Supervised Learning
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2018.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2024Piecing It All Together: Verifying Multi-Hop Multimodal Claimscited by 12
- 2026From Ground Truth to Measurement: A Statistical Framework for Human Labelingcited by 2
- 2024EdgeOL: Efficient in-situ Online Learning on Edge Devicescited by 1
- 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AIcited by 0
- 2026From Ground Truth to Measurement: A Statistical Framework for Human Labeling8 Apr 2026
- 2024Piecing It All Together: Verifying Multi-Hop Multimodal Claims14 Nov 2024
- 2024EdgeOL: Efficient in-situ Online Learning on Edge Devices30 Jan 2024
- 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI19 Jul 2022