Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- ar
+pipeline_tag: text-classification
+library_name: transformers
+base_model:
+- Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
+tags:
+- reranking
+- sentence-transformers
+datasets:
+- unicamp-dl/mmarco
 ---
 
+# GATE-Reranker-V1 🚀✨
+
+**NAMAA-space** releases **Rerankerv1**, 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! 📚🇸🇦
+
+This model is designed to **improve search relevance** of **arabic** documents by accurately ranking documents based on their contextual fit for a given query.
+
+## Key Features 🔑
+
+- **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.
+- **Advanced Document Ranking**: Ranks results with precision, perfect for search engines, recommendation systems, and question-answering applications.
+- **State-of-the-Art Performance**: Achieves excellent performance compared to famous rerankers(See [Evaluation](https://huggingface.co/NAMAA-Space/GATE-Reranker-V1#evaluation)), ensuring reliable relevance and precision.
+
+## Example Use Cases 💼
+
+- **Retrieval Augmented Generation**: Improve search result relevance for Arabic content.
+- **Content Recommendation**: Deliver top-tier Arabic content suggestions.
+- **Question Answering**: Boost answer retrieval quality in Arabic-focused systems.
+
+## Usage 
+
+# Within sentence-transformers
+The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
+
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('NAMAA-Space/Rerankerv1', max_length=512)
+
+Query = 'كيف يمكن استخدام التعلم العميق في معالجة الصور الطبية؟'
+Paragraph1 = 'التعلم العميق يساعد في تحليل الصور الطبية وتشخيص الأمراض'
+Paragraph2 = 'الذكاء الاصطناعي يستخدم في تحسين الإنتاجية في الصناعات'
+
+scores = model.predict([(Query, Paragraph1), (Query, Paragraph2)])
+```
+
+## Evaluation
+
+We evaluate our model on two different datasets using the metrics **MAP**, **MRR** and **NDCG@10**: 
+
+The purpose of this evaluation is to highlight the performance of our model with regards to: Relevant/Irrelevant labels and positive/multiple negatives documents:
+
+Dataset 1: [NAMAA-Space/Ar-Reranking-Eval](https://huggingface.co/datasets/NAMAA-Space/Ar-Reranking-Eval)
+
+
+
+Dataset 2: [NAMAA-Space/Arabic-Reranking-Triplet-5-Eval](https://huggingface.co/datasets/NAMAA-Space/Arabic-Reranking-Triplet-5-Eval)
+
+
+
+As seen, The model performs extremly well in comparison to other famous rerankers. 
+
 base model name : Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
-dataset : Arabic-mmarco-triplet ( 1 million random sample) 
+dataset : Arabic-mmarco-triplet ( 1 million random sample)