Sentence Similarity
PEFT
Safetensors
sentence-transformers
English
medical
cardiology
embeddings
domain-adaptation
lora
Instructions to use richardyoung/CardioEmbed-MPNet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richardyoung/CardioEmbed-MPNet-base with PEFT:
Task type is invalid.
- sentence-transformers
How to use richardyoung/CardioEmbed-MPNet-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richardyoung/CardioEmbed-MPNet-base") 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:
- 7b5471c190f50ff961e7fc1a4a200d422af22b15ec3e86a10cf7fd5ba9ce6350
- Size of remote file:
- 3.55 MB
- SHA256:
- 17e57ad9066d90adbb68c5825309a133eebbe50b8a61bd8acf53c7be187cd4fb
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