Sentence Similarity
sentence-transformers
Safetensors
English
German
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use headwAI/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use headwAI/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("headwAI/embeddinggemma-300m") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
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Update model card: Add GitHub link and refine pipeline tag for EmbeddingGemma
Verified signatureThis PR improves the model card for EmbeddingGemma by: - Updating the `pipeline_tag` from `sentence-similarity` to `feature-extraction` to better reflect the model's primary function as an embedding model that produces vector representations of text for various downstream tasks. This ensures it appears in relevant searches on the Hugging Face Hub for feature extraction models. The `sentence-similarity` tag is retained within the general `tags` list. - Adding a link to the official `google-gemini/gemma-cookbook` GitHub repository in the model card content, providing users with a comprehensive resource for examples and guides related to Gemma models, including EmbeddingGemma.bc6a400
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