Scikit-LLM enables few-shot text classification with LLMs via scikit-learn
Scikit-LLM is a Python library that lets developers use large language models (LLMs) for few-shot text classification through a scikit-learn interface. Unlike traditional machine learning workflows requiring thousands of labeled examples, few-shot classification relies on a small set of labeled inputs—often just three to five—to guide an LLM’s classification of new text. This approach is…
What you can do with it
AI at Work →Sort product reviews by sentiment with Scikit-LLM
Uses AI models via scikit-learn’s `fit()` and `predict()` to classify text based.
- Who for
- marketers
- Cost
- free
Use it for
- Categorize customer support queries
- Tag social media comments
- Sort product reviews by sentiment
How to set it up · From the article
- Install Scikit-LLM with `!pip install scikit-llm -q`
- Set Groq API key as a Colab secret named `GROQAPIKEY`
- Define labeled examples (`Xtrain`, `ytrain`) and new text (`Xtest`)
- Initialize `FewShotGPTClassifier` and call `fit()` then `predict()`
Watch out Check the details on the maker's page first
Key points from the news
- Scikit-LLM lets developers use LLMs for few-shot classification via scikit-learn’s `fit()` and `predict()` methods
- Only 3–5 labeled examples are needed to classify domain-specific text, like IT support tickets, without retraining
The library simplifies the process by wrapping LLMs behind familiar methods like fit() and predict(). In a step-by-step example using Groq’s free-tier LLM API, developers define a few labeled examples (e.g., ticket descriptions paired with categories) and pass them to a FewShotGPTClassifier. The fit() method constructs a prompt memory without retraining the model, while predict() classifies new, unlabeled text.
Few-Shot Text Classification with Scikit-LLM
KDnuggets · 14 October 2026
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This text was published by KDnuggets and written by Iván Palomares Carrascosa. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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