This article introduces Scikit-LLM, a Python library that integrates large language models like OpenAI's GPT with the Scikit-learn framework to simplify text analysis tasks. It explains and demonstrates two primary classification methods: zero-shot classification, which assigns labels based solely on the model's general knowledge without prior examples, and few-shot classification, which uses a small set of labeled examples within the prompt to improve accuracy. By following a Scikit-learn-style workflow using fit() and predict() methods, users can easily implement these advanced NLP techniques for tasks such as sentiment analysis and topic labeling.
