wizmap

Why we included this project

WizMap renders high-dimensional embedding spaces as a browsable map, so instead of staring at opaque vectors you can see how millions of data points actually cluster. It suits anyone who lives with embeddings day to day, whether that means NLP researchers checking sentence or document representations, teams auditing image and text embeddings from CLIP-style models, or analysts trying to figure out what a model has learned. The multi-resolution summarization keeps the view responsive at million-point scale, and the Python library lets you load your own embeddings with a couple of function calls, then render the result in a browser or inside Jupyter and Colab notebooks. Since maps are shareable via URL, it is also a handy way to walk collaborators through a dataset or model behavior without exporting static plots.