Instructions to use h2oai/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use h2oai/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("h2oai/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
- Xet hash:
- 6908edc901aaed0035679879dc13d50c3ea42b090e7dffa9b8031811e4beff6c
- Size of remote file:
- 9.44 MB
- SHA256:
- ffb6cc5162e11e2ce6bc2367e121ee3bbbc4e82e1ee26826bd7573d4948d81b8
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