Scikit-LLM Builds 2-Stage Multilingual Classifier With 85% Vaccine Test Case
Updated
Updated · KDnuggets · Nov 10
Scikit-LLM Builds 2-Stage Multilingual Classifier With 85% Vaccine Test Case
2 articles · Updated · KDnuggets · Nov 10
Summary
Scikit-LLM shows how to build a two-stage scikit-learn pipeline that first translates multilingual text into English, then classifies it with a zero-shot LLM.
The setup uses two Scikit-LLM wrappers—GPTTranslator and ZeroShotGPTClassifier—running through Groq’s OpenAI-compatible API with the same gpt-oss-20b model in both stages.
Only 3 unique labels—Energy, Finance and Healthcare—are needed for fitting, with placeholder inputs replacing a large labeled training set.
Test runs on Italian and Portuguese examples classified an 85% flu-vaccine study as Healthcare, a sodium-ion wind battery as Energy, and a tech-stock rebound as Finance.
The approach is pitched as a low-cost way to replace keyword matching with semantic reasoning while avoiding extra model training for multilingual classification.