Instructions to use gallerywise/coreml-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gallerywise/coreml-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gallerywise/coreml-embeddings") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5e44841390582d50e37b6bfb37a7429d8e22858c4a3708aa75f5178f8c76021
- Size of remote file:
- 41.3 MB
- SHA256:
- 405eb37ddf2dc22743599e19b42c2471af2bd4f46e1c4f3cd7561f7f2830c8be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.