Getting Started with Zero-Shot Text Classification

Zero-shot text classification allows models to categorize text without training on task-specific data by converting labels into natural language statements and checking if the input text supports each statement. Using pretrained models like facebook/bart-large-mnli, this approach treats classification as a reasoning problem rather than traditional labeling, making it ideal for rapid prototyping and situations where labeled training data doesn't exist.