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 training_config.json from richardyoung/CardioEmbed-BGE-M3: direct link, hf CLI and curl.
- Browser
- Download file 330 Bytes
-
https://huggingface.co/richardyoung/CardioEmbed-BGE-M3/resolve/main/training_config.json
- Command line
-
hf download hf://richardyoung/CardioEmbed-BGE-M3/training_config.json
-
curl -L -o training_config.json https://huggingface.co/richardyoung/CardioEmbed-BGE-M3/resolve/main/training_config.json
330 Bytes
| { | |
| "name": "bge-m3", | |
| "model_id": "BAAI/bge-m3", | |
| "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": "bge_m3_cardiology" | |
| } |