Zero-shot text classification is a task in natural language processing. In this task, a model is trained on a set of labeled examples. However, it is then able to classify new examples from previously unseen classes. It is important to note that zero-shot classification excludes any examples of the desired task being completed. This sets it apart from single or few-shot classification, which involve a single or a few examples of the selected task. 

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