Abid Ali Awan doesn’t have time to train:
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.
This typically works best when the set of classes is quite distinct and limited in number. Once you get past several classes, the likelihood of spurious results increases considerably and that’s when you’re back to model training/fine-tuning based off of sufficient quantities of labeled data.