Sentence Similarity
PEFT
Safetensors
sentence-transformers
English
medical
cardiology
embeddings
domain-adaptation
lora
Instructions to use richardyoung/CardioEmbed-BioLinkBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richardyoung/CardioEmbed-BioLinkBERT with PEFT:
Task type is invalid.
- sentence-transformers
How to use richardyoung/CardioEmbed-BioLinkBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richardyoung/CardioEmbed-BioLinkBERT") 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 adapter_model.safetensors from richardyoung/CardioEmbed-BioLinkBERT: direct link, hf CLI and curl.
- Browser
- Download file 9.46 MB
-
https://huggingface.co/richardyoung/CardioEmbed-BioLinkBERT/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://richardyoung/CardioEmbed-BioLinkBERT/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/richardyoung/CardioEmbed-BioLinkBERT/resolve/main/adapter_model.safetensors
9.46 MB
- Xet hash:
- 0b39df326f656db7ff5abc8713013f442536451ff463fb1dd5ffefe709268f53
- Size of remote file:
- 9.46 MB
- SHA256:
- 93a2689f97321200ced08a2389c4fcf79337df643456168667469041de7faddf
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