d-kleine/NER_decoder
Named Entity Recognition with an decoder-only (autoregressive) LLM using HuggingFace
This project helps machine learning engineers and researchers adapt large language models (LLMs) for Named Entity Recognition (NER) tasks. It takes raw text as input and identifies specific entities like people, organizations, and locations within it, producing tagged text as output. This is for those who want to experiment with using generative LLMs for understanding tasks.
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Use this if you are an AI/ML practitioner looking to explore how decoder-only LLMs like LLaMA can be fine-tuned for text understanding tasks such as Named Entity Recognition.
Not ideal if you need a production-ready, highly optimized, and thoroughly tested NER solution that generalizes well to new, unseen data, as this is a showcase project.
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Sep 10, 2025
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