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
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README.md
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license: gemma
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- text-embeddings-inference
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extra_gated_heading: Access EmbeddingGemma on Hugging Face
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extra_gated_prompt: To access EmbeddingGemma on Hugging Face, you’re required to review and
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agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
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Face and click below. Requests are processed immediately.
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extra_gated_button_content: Acknowledge license
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# EmbeddingGemma model card
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license: gemma
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---
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# EmbeddingGemma model card
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