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")
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]
https://huggingface.co/richardyoung/CardioEmbed-MPNet-base/resolve/main/tokenizer.json
hf download hf://richardyoung/CardioEmbed-MPNet-base/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/richardyoung/CardioEmbed-MPNet-base/resolve/main/tokenizer.json
