Sentence Similarity
LiteRT
sentence-transformers
feature-extraction
text-embeddings-inference
litertlm
quantized
int8
Instructions to use technotic/embeddinggemma-300M-litertlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use technotic/embeddinggemma-300M-litertlm with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sentence-transformers
How to use technotic/embeddinggemma-300M-litertlm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("technotic/embeddinggemma-300M-litertlm") 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
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!