Instructions to use mohamed2811/Muffakir_Embedding_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mohamed2811/Muffakir_Embedding_V2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mohamed2811/Muffakir_Embedding_V2") 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:
- 3d29ee37f05f384ed80523b0c15e2cbde86b7c3914d7035bb2359fb0088ab47b
- Size of remote file:
- 5.62 kB
- SHA256:
- 4d0af54f1d867662205005dca9657ece93833d39cc5b2f2626e4b1d0728b36f3
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