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:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2") model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-MPNet-base") - 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
File size: 964 Bytes
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