Sentence Similarity
PEFT
Safetensors
sentence-transformers
English
medical
cardiology
embeddings
domain-adaptation
lora
Instructions to use richardyoung/CardioEmbed-MPNet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richardyoung/CardioEmbed-MPNet-base with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2") model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-MPNet-base") - sentence-transformers
How to use richardyoung/CardioEmbed-MPNet-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richardyoung/CardioEmbed-MPNet-base") 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: 363 Bytes
c2f37c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"name": "all-mpnet-base-v2",
"model_id": "sentence-transformers/all-mpnet-base-v2",
"batch_size": 48,
"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": [
"q",
"k",
"v"
],
"output_dir": "mpnet_base_cardiology"
} |