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
add top language nav (English/繁體中文 jump)
Browse files
README.md
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# IlhaEmbed
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A small embedding model that reads Taiwanese clinical text.
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These are words that clinical staff in Taiwan actually write in charts, remarks, and community-care sheets. 斷腦筋 means stroke. 不辣咖 means a blood culture. H/T is hypertension, and V(H/T) means the patient is seen at an outside clinic for hypertension. General-purpose models and general Chinese models read none of these, because they never learned how doctors, nurses, and care workers in Taiwan really write. IlhaEmbed learned it.
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# IlhaEmbed
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**English** | [**繁體中文**](#繁體中文)
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A small embedding model that reads Taiwanese clinical text.
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These are words that clinical staff in Taiwan actually write in charts, remarks, and community-care sheets. 斷腦筋 means stroke. 不辣咖 means a blood culture. H/T is hypertension, and V(H/T) means the patient is seen at an outside clinic for hypertension. General-purpose models and general Chinese models read none of these, because they never learned how doctors, nurses, and care workers in Taiwan really write. IlhaEmbed learned it.
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