Feature Extraction
sentence-transformers
Safetensors
qwen2
medical
custom_code
text-embeddings-inference
Instructions to use Singaraj/sante-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Singaraj/sante-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Singaraj/sante-embed", trust_remote_code=True) 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
Download tokenizer.json from Singaraj/sante-embed: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Singaraj/sante-embed/resolve/main/tokenizer.json
- Command line
-
hf download hf://Singaraj/sante-embed/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Singaraj/sante-embed/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 0eb8a710e1226a32c8bb3b4660f4029f7acfd5ce67047fabb73c0e35b08e1e03
- Size of remote file:
- 11.4 MB
- SHA256:
- 2f79052deba517b0663d877714e117a31a4a6243cddb85fc4443c80a2fa65a20
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