Instructions to use unsloth/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use unsloth/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("unsloth/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] - Inference
- Notebooks
- Google Colab
- Kaggle
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- sentence-similarity
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- feature-extraction
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- text-embeddings-inference
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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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- sentence-similarity
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- feature-extraction
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- text-embeddings-inference
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base_model:
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- google/embeddinggemma-300M
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# EmbeddingGemma model card
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