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:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("michiyasunaga/BioLinkBERT-large") model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-BioLinkBERT") - 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
Standardize author sign-off
Browse files
README.md
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## Related Models
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This is part of the CardioEmbed model family. See [richardyoung/CardioEmbed](https://huggingface.co/richardyoung/CardioEmbed) for more models.
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## Related Models
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This is part of the CardioEmbed model family. See [richardyoung/CardioEmbed](https://huggingface.co/richardyoung/CardioEmbed) for more models.
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*Built & maintained by [Richard Young](https://deepneuro.ai/richard) 路 DeepNeuro*
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