NER: Gemini vs Spacy vs Compromise

TLDR For NER , if accuracy is critical, go with an LLM — even an old one like gemma-3-27b-it will outperform tools or small models trained for this task. But by using an LLM you are exposing your data, making an HTTP request, and most likely incurring a cost. If accuracy is not critical and you want to stay in Javascript, compromise is a good package for NER. If you want an even better package and it's OK not using Javascript, then try Spacy . Intro Thanks to AI it feels like we are entering into Web 4 — now it's not just about having a static website (Web 1), or letting the user save their data (Web 2), or decentralization (Web 3, although the majority of companies still own our data); but about adding "AI" to your application. What does that mean? Are all applications supposed to be like ChatGPT? While I don't think adding an LLM just for the sake of it is beneficial, I do think that playing with LLMs helps to learn about them, giving us an understanding of how, when, and why to use