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
| { | |
| "name": "BioLinkBERT-large", | |
| "model_id": "michiyasunaga/BioLinkBERT-large", | |
| "batch_size": 64, | |
| "gradient_accumulation_steps": 1, | |
| "learning_rate": 2e-05, | |
| "epochs": 3, | |
| "max_seq_length": 512, | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "use_8bit": false, | |
| "target_modules": [ | |
| "query", | |
| "key", | |
| "value" | |
| ], | |
| "output_dir": "biolinkbert_cardiology" | |
| } |