CLIP · ViT-L/14 · Foundation Model

Embedding Space Explorer

Edit any cluster's vocabulary, then run real Gemini embeddings + PCA — semantically related words collapse to nearby points in 2D.

Execution pipeline
  1. 1Tokenize words
  2. 2Encode via embedding model
  3. 3Center & normalize
  4. 4PCA on Gram matrix
  5. 5Project to 2D
Projection
PCA · 2D embedding map
Click compute to generate real embeddings.
Clusters