Summary
Classification that runs locally and returns confidence, not a guess.
Static embeddings mean the work happens on your machine with nothing sent out, and the categories are yours to define — so the labels match your taxonomy rather than a fixed list someone else chose.
What it is
A text classifier built on Model2Vec static embeddings: classify one text or many, and manage the category set the classifier works against.
What you get
- A single text classified, with confidence scores attached
- Many texts classified in one call
- Ten categories out of the box — technology, business, health, sports, entertainment, politics, science, education, travel and food
- Your own categories added, one at a time or in a batch, each with a description
- The current category list read back, and categories removed when they stop being useful
- Category list and model information also available as resources
Requirements
Python 3.10 or newer and the uv package manager. There is no install step — dependencies are declared inline in the script and resolved on run. It speaks stdio locally, and HTTP/SSE or Streamable HTTP when you want it remote.