Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use h2oai/embeddinggemma-300m-qat-q8_0-unquantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use h2oai/embeddinggemma-300m-qat-q8_0-unquantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("h2oai/embeddinggemma-300m-qat-q8_0-unquantized") 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:
- 3e3845b10ba7c713cf16944541fcbdbdcea1d400e9596b0574620cec400c7f62
- Size of remote file:
- 9.44 MB
- SHA256:
- e5518d9b40a5e2f03725104048a30a9b0dd4d2d5e92c954f046d30999c55e26b
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