Zero-Shot Classification
Classifying text into categories that the model was never explicitly trained on, using only the category names or descriptions as guidance.
Why It Matters
Zero-shot classification eliminates the need for labeled training data for new categories. You can add new classes instantly by just naming them.
Example
Classifying customer emails into 'billing,' 'technical,' 'shipping,' and 'praise' categories without any labeled training examples — the model understands the categories from their names.
Think of it like...
Like a new employee who can sort mail into labeled bins on their first day — they understand the labels without being trained on example letters for each bin.
Related Terms
Zero-Shot Learning
A model's ability to perform a task it was never explicitly trained on or shown examples of. The model applies its general knowledge and reasoning to handle entirely new task types.
Classification
A type of supervised learning task where the model predicts which category or class an input belongs to. The output is a discrete label rather than a continuous value.
Natural Language Inference
The NLP task of determining the logical relationship between two sentences — whether one entails, contradicts, or is neutral with respect to the other.
Text Classification
The NLP task of assigning predefined categories or labels to text documents. It is one of the most common and commercially important NLP applications.
Large Language Model
A type of AI model trained on massive amounts of text data that can understand and generate human-like text. LLMs use transformer architecture and typically have billions of parameters, enabling them to perform a wide range of language tasks.