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--- |
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license: apache-2.0 |
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language: |
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- ar |
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pipeline_tag: text-classification |
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library_name: transformers |
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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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- reranking |
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- sentence-transformers |
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datasets: |
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- unicamp-dl/mmarco |
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--- |
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# Namaa-Reranker-v1 🚀✨ |
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**NAMAA-space** releases **Namaa-Reranker-v1**, a high-performance model fine-tuned on [unicamp-dl/mmarco](https://huggingface.co/datasets/unicamp-dl/mmarco) to elevate Arabic document retrieval and ranking to new heights! 📚🇸🇦 |
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This model is designed to **improve search relevance** of **arabic** documents by accurately ranking documents based on their contextual fit for a given query. |
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## Key Features 🔑 |
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- **Optimized for Arabic**: Built on the highly performant [Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2](https://huggingface.co/Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2) with exclusivly rich Arabic data. |
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- **Advanced Document Ranking**: Ranks results with precision, perfect for search engines, recommendation systems, and question-answering applications. |
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- **State-of-the-Art Performance**: Achieves excellent performance compared to famous rerankers(See [Evaluation](https://huggingface.co/NAMAA-Space/Rerankerv1#evaluation)), ensuring reliable relevance and precision. |
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## Example Use Cases 💼 |
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- **Retrieval Augmented Generation**: Improve search result relevance for Arabic content. |
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- **Content Recommendation**: Deliver top-tier Arabic content suggestions. |
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- **Question Answering**: Boost answer retrieval quality in Arabic-focused systems. |
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## Usage |
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# Within sentence-transformers |
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The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: |
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```python |
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from sentence_transformers import CrossEncoder |
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model = CrossEncoder('NAMAA-Space/Namaa-Reranker-v1', max_length=512) |
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Query = 'كيف يمكن استخدام التعلم العميق في معالجة الصور الطبية؟' |
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Paragraph1 = 'التعلم العميق يساعد في تحليل الصور الطبية وتشخيص الأمراض' |
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Paragraph2 = 'الذكاء الاصطناعي يستخدم في تحسين الإنتاجية في الصناعات' |
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scores = model.predict([(Query, Paragraph1), (Query, Paragraph2)]) |
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``` |
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## Evaluation |
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We evaluate our model on two different datasets using the metrics **MAP**, **MRR** and **NDCG@10**: |
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The purpose of this evaluation is to highlight the performance of our model with regards to: Relevant/Irrelevant labels and positive/multiple negatives documents: |
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Dataset 1: [NAMAA-Space/Ar-Reranking-Eval](https://huggingface.co/datasets/NAMAA-Space/Ar-Reranking-Eval) |
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Dataset 2: [NAMAA-Space/Arabic-Reranking-Triplet-5-Eval](https://huggingface.co/datasets/NAMAA-Space/Arabic-Reranking-Triplet-5-Eval) |
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As seen, The model performs extremly well in comparison to other famous rerankers. |
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WIP: More comparisons and evaluation on arabic datasets. |
