Sentence Similarity
PEFT
Safetensors
sentence-transformers
English
medical
cardiology
embeddings
domain-adaptation
lora
Instructions to use richardyoung/CardioEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richardyoung/CardioEmbed with PEFT:
Task type is invalid.
- sentence-transformers
How to use richardyoung/CardioEmbed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richardyoung/CardioEmbed") 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:
- f48d3bee242452534228953df5f981c4a3df6bfe7bb39371ca6f47fc0ec21e2d
- Size of remote file:
- 123 MB
- SHA256:
- 3b6289420ce6f8efe3ee6118de5f9d2f58e6eb79ee9fd100d1deb95137dca531
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