Feature Extraction
ONNX
sentence-transformers
onnxruntime
bert
int8
quantized
biomedical
embeddings
justembed
Instructions to use sekarkrishna/sapbert-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sekarkrishna/sapbert-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sekarkrishna/sapbert-int8") 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:
- 771a5b3405b874c2b9ad3a783048d20151103fdf55affbf7baa73504719c9915
- Size of remote file:
- 110 MB
- SHA256:
- 5bcbd5b1de3239bbebdb9b56ac0de19f84d3d3b78ecd21358c423703d67040f0
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