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language: |
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- ar |
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base_model: |
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- Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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--- |
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### Model Summary: |
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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. |
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### Dataset: |
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- The dataset used for training is derived from **Egyptian law books**. |
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- It consists of **synthetic data** generated using a **Large Language Model (LLM)**. |
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- The dataset contains **20,252 samples**, formatted as **question-answer pairs**. |
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### Key Features: |
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- **Vector Representation:** 768-dimensional embeddings. |
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- **Training Loss:** MatryoshkaLoss & MultipleNegativesRankingLoss. |
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- **Evaluation Metrics:** Cosine similarity-based metrics (Accuracy, Precision, Recall, NDCG). |
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## 🏆 Leaderboard Performance |
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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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These results underscore the model's effectiveness in both retrieving relevant information and accurately ranking it within Arabic Retrieval-Augmented Generation (RAG) systems. |
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This model is optimized for **legal document retrieval** and other NLP applications in Arabic. |
