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For many automated classification tasks, collecting labeled data is the key barrier to training a useful supervised model. Interfaces for interactive labeling tighten the loop of labeled data collection and model development, enabling a subject-matter expert to quickly establish the feasibility of a classifier to address a problem of interest.
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