Update README.md

README.md

@@ -18,21 +18,17 @@ tags:
 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 with rich Arabic data, this model understands both Modern Standard Arabic (MSA) and diverse dialects, making it highly effective across various Arabic-speaking regions.
 - **Advanced Document Ranking**: Ranks results with precision, perfect for search engines, recommendation systems, and question-answering applications.
-- **State-of-the-Art Performance**: Achieves exceptional benchmarks on Arabic datasets ((See [Evaluation](https://huggingface.co/omarelshehy/Arabic-STS-Matryoshka#evaluation))), ensuring reliable relevance and precision.
-
-Whether you’re looking to enhance Arabic search results, improve information retrieval, or develop an intelligent Arabic chatbot, the NAMAA Space Reranker is here to support your journey! 🌐✨
+- **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 💼
-- **Search Engine Ranking**: Improve search result relevance for Arabic content.
+
+- **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.
 
-## Get Started 🚀
-Load and test the NAMAA Space Reranker today and bring accurate, context-aware Arabic ranking to your project!
-
-
 ## Usage 
 
 # Within sentence-transformers
@@ -50,3 +46,5 @@ scores = model.predict([(Query, Paragraph1), (Query, Paragraph2)])
 ```
 
 ## Evaluation
+
+