Sergidev/EmbeddingGemma-3d

🌌 3D Embed — EmbeddingGemma Visualizer

An interactive demo showcasing Google's EmbeddingGemma 300M — a state-of-the-art, lightweight embedding model built on the Gemma 3 architecture.

This space lets you see how the model understands language by projecting its 768-dimensional embeddings into explorable 3D space using PCA dimensionality reduction.

✨ Features

• —🔤 Word Galaxy — Type individual words and watch semantic clusters form in 3D. See how "cat", "dog", and "fish" cluster differently from "car", "bus", and "train".
• —📝 Sentence Explorer — Compare full sentences and discover how meaning shapes geometry. Similar sentences land near each other; different ones drift apart.
• —🔍 Semantic Search — Enter a query and a set of documents. Watch the model find the closest match and see why through spatial proximity.
• —🪆 Matryoshka Dimensions — Explore how MRL (Matryoshka Representation Learning) lets you truncate embeddings from 768d → 512d → 256d → 128d with minimal quality loss, visualized side-by-side.

🧠 About EmbeddingGemma

EmbeddingGemma is the highest-ranking text-only multilingual embedding model under 500M parameters on the MTEB leaderboard. It uses bidirectional attention (encoder-style) rather than causal decoding, making it purpose-built for embeddings.

🚀 How It Works

1. 1.Text is passed through EmbeddingGemma with task-specific prompts (e.g., "task: sentence similarity | query: ")
2. 2.The model produces 768-dimensional normalized embeddings
3. 3.PCA reduces these to 3 dimensions, capturing the directions of maximum variance
4. 4.Plotly renders the points as an interactive 3D scatter plot

📚 References

• —EmbeddingGemma Blog Post
• —Model Card — google/embeddinggemma-300m
• —EmbeddingGemma Technical Report
• —Sentence Transformers Documentation
• —Hugging Face Spaces Docs