{"version":1,"type":"story","url":"https://digestai.news/story/scikit-llm-enables-custom-ner-pipelines-using-free-groq-models","json":"https://digestai.news/story/scikit-llm-enables-custom-ner-pipelines-using-free-groq-models.json","markdown":"https://digestai.news/story/scikit-llm-enables-custom-ner-pipelines-using-free-groq-models.md","slug":"scikit-llm-enables-custom-ner-pipelines-using-free-groq-models","headline":"Scikit-LLM enables custom NER pipelines using free Groq models","summary":"A new tutorial demonstrates how to build custom Named Entity Recognition (NER) pipelines using the Scikit-LLM library. The guide shows developers how to leverage Large Language Model (LLM) reasoning to extract structured data from unstructured text without the need for extensive training datasets or fine-tuning. \n\nThe example uses Groq's free-tier API to run the open-source model `openai/gpt-oss-20b` within a Google Colab notebook. By defining a simple entity dictionary with descriptions, the `GPTExplainableNER` estimator processes raw text to identify categories such as executives, corporations, products, and cities. The results show that while raw LLM outputs can be messy, simple post-processing with regular expressions yields clean, structured data. \n\nThis approach offers a low-code alternative to traditional NLP libraries, allowing users to implement domain-specific extraction tasks with minimal configuration. The method relies on the reasoning capabilities of pre-trained models rather than supervised learning, making it accessible for quick prototyping and data analysis tasks.","keyPoints":["Scikit-LLM allows building custom NER pipelines using LLM reasoning without fine-tuning.","The tutorial uses Groq's free API and the openai/gpt-oss-20b model in Google Colab.","Raw LLM outputs require regex post-processing to achieve clean, structured entity extraction."],"whyItMatters":"It provides a low-cost, low-code method for extracting structured data from text, reducing the barrier to entry for NLP tasks that traditionally require large datasets and specialized expertise.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":["Groq","Microsoft","Tesla","Nvidia"],"models":["openai/gpt-oss-20b"],"people":["Iván Palomares Carrascosa","Satya Nadella","Elon Musk","Jensen Huang"]},"firstPublishedAt":"2026-10-21T14:00:00Z","updatedAt":"2026-10-21T14:00:00Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"KDnuggets","title":"Named Entity Recognition Using Scikit-LLM","url":"https://kdnuggets.com/named-entity-recognition-using-scikit-llm","publishedAt":"2026-10-21T14:00:00Z","type":"press","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":{"title":"Scikit-LLM Expands Machine Learning Integration","url":"https://digestai.news/thread/scikit-llm-enables-few-shot-text-classification-with-llms-via-scikit-learn","storyCount":2},"cite":{"text":"Digest AI, \"Scikit-LLM enables custom NER pipelines using free Groq models\", 21 October 2026, https://digestai.news/story/scikit-llm-enables-custom-ner-pipelines-using-free-groq-models","publisher":"Digest AI","title":"Scikit-LLM enables custom NER pipelines using free Groq models","datePublished":"2026-10-21T14:00:00Z","url":"https://digestai.news/story/scikit-llm-enables-custom-ner-pipelines-using-free-groq-models"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}