Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:439290
loss:DualThresholdEnforcedMNRL1
text-embeddings-inference
Instructions to use Auto-opts/flax-TMNRLB_CVR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Auto-opts/flax-TMNRLB_CVR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Auto-opts/flax-TMNRLB_CVR") sentences = [ "compression therapy benefits", "edema: what is, causes, symptoms, and treatment", "How VIN Data Enhances Market Value Assessments", "Daily Iron Intake from Leafy Greens and Fortified Cereals" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle