See your data in its true shape.

HyperView is open-source dataset curation across multiple geometries. An AI copilot finds label noise, hierarchy errors, and long-tail issues. Multimodal native.

multi-geometry supportAI curation copilotmultimodal native

View on GitHub Try demo

$ pip install hyperview && hyperview demo

Why geometry matters

Hierarchical data gets crowded in Euclidean projections. The crowding problem means there isn't enough room to keep fine-grained structure separated. Rare modes collapse into dominant clusters—representation collapse.

Hyperbolic space has exponential volume growth. That matches hierarchy.

Euclidean (flat)

V(r) ~ r^d

Polynomial growth. Rare subgroups overlap.

see transformation →

Hyperbolic (curved)

V(r) ~ e^r

Exponential growth. Hierarchy preserved.

see the difference →

HyperView lets you toggle Euclidean ↔ hyperbolic (Poincaré disk) on the same dataset.

Projects

Open source. Code is MIT; packages on PyPI/npm. Model weights may carry upstream licenses.

HyperView

Data curation co-pilot

Multi-panel curation UI: image grid + embedding map. Euclidean ↔ Poincaré disk.

→Agentic data cleanup
→Multi-geometry views
→HuggingFace integration

pip install hyperviewdemo

hyper-scatter

WebGL scatterplot engine

Pure WebGL2 scatterplot for Euclidean + Poincaré disk with Möbius-correct interactions.

→Möbius pan/zoom
→Geodesic-aware lasso
→20M points @ 60 FPS

npm i hyper-scatterdemo

hyper-models

Embedding model zoo

Non-Euclidean embedding encoders with simple API and torch-free ONNX runtime.

→Hyperbolic encoders
→Torch-free ONNX
→Auto HF download

pip install hyper-modelsdemo