google/embeddinggemma-300m
EmbeddingGemma is a 300M parameter multilingual open embedding model from Google DeepMind, designed for efficient deployment even on low-resource devices, producing high-quality text vector representations for tasks such as search, classification, clustering, and semantic similarity.
Try the model with a sample request using the same API shape shown in the reference page.
You can add more items with the button on the right
The service tier used for processing the request. When set to 'priority', the request will be processed with higher priority (only applies to models that support it).
Enter a JSON array
whether to normalize the computed embeddings
The number of dimensions in the embedding. If not provided, the model's default will be used.If provided bigger than model's default, the embedding will be padded with zeros. (Default: empty, 32 ≤ dimensions ≤ 8192)
Adjust settings and run the demo to see the model output generated here in real-time.
