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 1_Pooling/config.json from Singaraj/sante-embed: direct link, hf CLI and curl.
- Browser
- Download file 96 Bytes
-
https://huggingface.co/Singaraj/sante-embed/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Singaraj/sante-embed/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Singaraj/sante-embed/resolve/main/1_Pooling/config.json
96 Bytes
| { | |
| "embedding_dimension": 1536, | |
| "pooling_mode": "lasttoken", | |
| "include_prompt": true | |
| } |