Sentence Similarity
PEFT
Safetensors
sentence-transformers
English
medical
cardiology
embeddings
domain-adaptation
lora
Instructions to use richardyoung/CardioEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richardyoung/CardioEmbed with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3-Embedding-8B") model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed") - sentence-transformers
How to use richardyoung/CardioEmbed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richardyoung/CardioEmbed") 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
File size: 590 Bytes
fabf094 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"model": "Qwen3-8B",
"model_id": "Qwen/Qwen3-Embedding-8B",
"training_samples": 106386,
"training_time_minutes": 658.5636151166667,
"test_results": {
"positive_sim_mean": 0.9083688259124756,
"positive_sim_std": 0.06640379875898361,
"retrieval_acc@1": 1.0,
"retrieval_acc@5": 1.0,
"test_samples": 2000
},
"config": {
"name": "Qwen3-8B",
"model_id": "Qwen/Qwen3-Embedding-8B",
"batch_size": 128,
"learning_rate": 0.0002,
"epochs": 2,
"output_dir": "qwen3_8bit_cardiology_full_106k"
},
"timestamp": "2025-11-08T08:37:14.153004"
} |