Sentence Similarity
sentence-transformers
ONNX
Safetensors
Chinese
English
modernbert
embeddings
medical
clinical-nlp
healthcare
biomedical
fhir
traditional-chinese
taiwan
rag
text-embeddings-inference
Instructions to use weemed/IlhaEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use weemed/IlhaEmbed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("weemed/IlhaEmbed") sentences = [ "帶狀皰疹", "皮蛇", "高血壓用藥", "114年成健" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
release: update IlhaEmbed with TAIDE-LX-7B 3-epoch knowledge distillation & INT8 ONNX (36.78MB)
823a8a4 verified Download model.onnx from weemed/IlhaEmbed: direct link, hf CLI and curl.
- Browser
- Download file 153 MB
-
https://huggingface.co/weemed/IlhaEmbed/resolve/main/model.onnx
- Command line
-
hf download hf://weemed/IlhaEmbed/model.onnx
-
curl -L -o model.onnx https://huggingface.co/weemed/IlhaEmbed/resolve/main/model.onnx
153 MB
- Xet hash:
- 66864fceb026198a19b22a93f25e7459f8036d4e065da8d43bb8c40f9b430388
- Size of remote file:
- 153 MB
- SHA256:
- af960d3ee4c06ff3ac12c993259d99945aa8f33a11b837c484fd99e84f9a34d8
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