Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:148
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use MossaabDev/quran_shifaa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MossaabDev/quran_shifaa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MossaabDev/quran_shifaa") sentences = [ "I live in a very bad country, I wish I live in another country", "O believers! Patiently endure, persevere, stand on guard, and be mindful of Allah, so you may be successful.", "But perhaps you hate a thing and it is good for you; and perhaps you love a thing and it is bad for you. And Allah knows, while you know not", "Do not do a favour expecting more ?in return?." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle