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
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The **Muffakir\_Embedding** model has achieved notable rankings on the [Arabic RAG Leaderboard](https://huggingface.co/spaces/Navid-AI/The-Arabic-Rag-Leaderboard), securing:
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The **Muffakir\_Embedding** model has achieved notable rankings on the [Arabic RAG Leaderboard](https://huggingface.co/spaces/Navid-AI/The-Arabic-Rag-Leaderboard), securing:
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**🥇 1th place** in the **Islamic Dataset**
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