Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:2437
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ayaat/mpnet-base-all-mqp-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ayaat/mpnet-base-all-mqp-binary with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ayaat/mpnet-base-all-mqp-binary") sentences = [ "I am having troubles and confusing moments with my body and I am scared I may be pregnant by my research online and I really want some advice ?", "Does Acyclovir cause ulcers when it is prescribed for genital herpes? ", "The confusing symptoms and online research points towards me being pregnant. Can I get a professional advice?", "Do bariatric surgeries like gastric sleeve or Roux-en-Y surgery actually work in the long term?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K