TAG. LABEL. CATEGORISE.
Successful data analysis relies heavily on the metadata or tags associated with your dataset, and in particular to develop supervised machine learning models you need high-quality training and evaluation data, both for the model’s initial construction and throughout its lifecycle.
But there’s no need to burden your data analysts or data scientists with this task. Whether you need simple binary classification of images, multi-level document classification, identification of themes, or the categorisation of sentiment from dense natural text, Hivemind can help.
Classifying text or documents into user-defined categories is a popular use case for Hivemind CORE — for instance when organising or triaging archives of internal research notes or other documents. This demo video explains how to set up a document classification task, using CORE.