Sentence Similarity
sentence-transformers
ONNX
Safetensors
Chinese
modernbert
embeddings
clinical
healthcare
traditional-chinese
taiwan
medical
fhir
on-premise
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 = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8fa1a2cf2c6c727a7c93caee62ec99b3bebc826ae27c2dc8af69ced002428ac
- Size of remote file:
- 38.5 MB
- SHA256:
- 3449f8bade1c8836323c9e5b802a48c35be97ced9fafcb7c16a2fe2d471a2705
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