News Summary

• 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.
• This makes zero-shot classification especially useful when you want to test an idea quickly, work with changing label sets, or build a lightweight prototype before investing in supervised training.