Sentence Similarity
PEFT
Safetensors
sentence-transformers
English
medical
cardiology
embeddings
domain-adaptation
lora
Instructions to use richardyoung/CardioEmbed-BGE-M3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richardyoung/CardioEmbed-BGE-M3 with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("BAAI/bge-m3") model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-BGE-M3") - sentence-transformers
How to use richardyoung/CardioEmbed-BGE-M3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richardyoung/CardioEmbed-BGE-M3") 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
Download tokenizer.json from richardyoung/CardioEmbed-BGE-M3: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/richardyoung/CardioEmbed-BGE-M3/resolve/main/tokenizer.json
- Command line
-
hf download hf://richardyoung/CardioEmbed-BGE-M3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/richardyoung/CardioEmbed-BGE-M3/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- f1e2d0633986c69eef4b87e00f5dd8956c23702b2ab002db0b14ab3bbd71ae06
- Size of remote file:
- 17.1 MB
- SHA256:
- d9a6af42442a3e3e9f05f618eae0bb2d98ca4f6a6406cb80ef7a4fa865204d61
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