Build a Named Entity Recognition App with Streamlit

In my previous article, we fine-tuned a Named Entity Recognition (NER) model, trained on the wnut_17[1] dataset. In this article, we show step-by-step how to integrate this model with Streamlit and deploy it using HugginFace Spaces. The goal of this app is to tag input sentences per user request in real time. Also, keep in mind, that contrary to trivial ML models, deploying a large language model on Streamlit is tricky. We also address those challenges.