Sentence Similarity
PEFT
Safetensors
sentence-transformers
English
medical
cardiology
embeddings
domain-adaptation
lora
Instructions to use richardyoung/CardioEmbed-Qwen3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richardyoung/CardioEmbed-Qwen3-4B with PEFT:
Task type is invalid.
- sentence-transformers
How to use richardyoung/CardioEmbed-Qwen3-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richardyoung/CardioEmbed-Qwen3-4B") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d5dec43fe2e5ba676ecd964d5f36fd46a63530b2e4096abee430d05346b348b
- Size of remote file:
- 11.4 MB
- SHA256:
- 00bc7e8d1c2c18e5ced697f8b4beb4e4e8f4285180ffbe6b51d1b46d12cc9a75
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