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---
language:
- ar
base_model:
- Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
tags:
- sentence-transformers
- sentence-similarity
---
### Model Summary:
This model is a **Sentence Transformer** based on **Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2**, fine-tuned for **semantic textual similarity** and **information retrieval** tasks. It maps sentences to dense vector representations for tasks like search, clustering, and text classification.
### Dataset:
- The dataset used for training is derived from **Egyptian law books**.
- It consists of **synthetic data** generated using a **Large Language Model (LLM)**.
- The dataset contains **20,252 samples**, formatted as **question-answer pairs**.
### Key Features:
- **Vector Representation:** 768-dimensional embeddings.
- **Training Loss:** MatryoshkaLoss & MultipleNegativesRankingLoss.
- **Evaluation Metrics:** Cosine similarity-based metrics (Accuracy, Precision, Recall, NDCG).
---
## 🏆 Leaderboard Performance
The **Muffakir\_Embedding** model has achieved notable rankings on the [Arabic RAG Leaderboard](https://huggingface.co/spaces/Navid-AI/The-Arabic-Rag-Leaderboard), securing:
**🥇 1th place** in the **Islamic Dataset**
These results underscore the model's effectiveness in both retrieving relevant information and accurately ranking it within Arabic Retrieval-Augmented Generation (RAG) systems.
---
This model is optimized for **legal document retrieval** and other NLP applications in Arabic. |