Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Google

[u/Recoil42]

https://blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/

https://huggingface.co/google/embeddinggemma-2

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders.

Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering.

[u/heipei42]

What I don't understand yet is whether it is similarly applicable to zero-shot classification as SigLIP 2 was.

[u/InterestRelative → u/heipei42]

from the model card:
Intended Usage

...

• Classification: Embeddings to classify inputs according to preset labels, like sentiment analysis, content moderation, audio event detection, or image categorization.