Scikit-LLM Builds Custom NER Pipelines With 4 Entity Types Using GPT-OSS-20B
Updated
Updated · KDnuggets · Oct 21
Scikit-LLM Builds Custom NER Pipelines With 4 Entity Types Using GPT-OSS-20B
1 articles · Updated · KDnuggets · Oct 21
Summary
Scikit-LLM can turn unstructured text into custom named-entity outputs in a few steps by wrapping an LLM with its GPTExplainableNER estimator and an entity dictionary.
The example configures Scikit-LLM to use Groq's OpenAI-compatible API, then runs the free-tier openai/gpt-oss-20b model on 3 tech-business sentences covering executives, corporations, products and cities.
Raw model responses were verbose and inconsistently formatted, so the workflow adds Python parsing and regex post-processing to convert them into clean labels such as Satya Nadella, Copilot, Microsoft and Seattle.
The article positions the approach as a lighter alternative to traditional NER systems, which often need large training datasets and fine-tuning to recognize domain-specific entities.