In this article, you will learn how zero-shot text classification works and how to apply it using a pretrained transformer model. Topics we will cover include: The core idea behind zero-shot classification and how it reframes labeling as a reasoning task. How to use a pretrained model to classify text without task-specific training data. Practical techniques such as multi-label classification and hypothesis template tuning. Let’s get started. Getting Started with Zero-Shot Text Classification Image by Editor Introduction Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset. Instead of collecting examples for every category you want, you provide the model with a piece of text and a list of possible labels. The model then decides which label fits best based on its general language understanding.…