Instructions to use mohamed2811/Muffakir_Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mohamed2811/Muffakir_Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mohamed2811/Muffakir_Embedding") sentences = [ "هذا شخص سعيد", "هذا كلب سعيد", "هذا شخص سعيد جدا", "اليوم هو يوم مشمس" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fca2d14ad2626b51ee2356072e1c24307430379546344eca1b8042819319edc
- Size of remote file:
- 5.56 kB
- SHA256:
- a94c44eb80a75a6bf64112fe418a0d995105175028bb9a26b141abd8ec356d2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.