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:
Task type is invalid.
- 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
| { | |
| "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" | |
| } |