Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:9306
loss:CoSENTLoss
text-embeddings-inference
Instructions to use s2593817/sft-question-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use s2593817/sft-question-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("s2593817/sft-question-embedding") sentences = [ "What are the name, population, and life expectancy of the largest Asian country by land?", "Find the names and phone numbers of customers living in California state.", "What is the age of the doctor named Zach?", "What are the name and location of the cinema with the largest capacity?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle