Update README.md

README.md

@@ -13,9 +13,9 @@ datasets:
 - unicamp-dl/mmarco
 ---
 
-# GATE-Reranker-V1 🚀✨
+# Namaa-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! 📚🇸🇦
+**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! 📚🇸🇦
 
 This model is designed to **improve search relevance** of **arabic** documents by accurately ranking documents based on their contextual fit for a given query.
 
@@ -38,7 +38,7 @@ The usage becomes easier when you have [SentenceTransformers](https://www.sbert.
 
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('NAMAA-Space/Rerankerv1', max_length=512)
+model = CrossEncoder('NAMAA-Space/Namaa-Reranker-v1', max_length=512)
 
 Query = 'كيف يمكن استخدام التعلم العميق في معالجة الصور الطبية؟'
 Paragraph1 = 'التعلم العميق يساعد في تحليل الصور الطبية وتشخيص الأمراض'
@@ -55,11 +55,11 @@ The purpose of this evaluation is to highlight the performance of our model with
 
 Dataset 1: [NAMAA-Space/Ar-Reranking-Eval](https://huggingface.co/datasets/NAMAA-Space/Ar-Reranking-Eval)
 
-
+![Plot](https://huggingface.co/NAMAA-Space/Namaa-Reranker-v1/resolve/main/Dataset1_Evaluation.jpg)
 
 Dataset 2: [NAMAA-Space/Arabic-Reranking-Triplet-5-Eval](https://huggingface.co/datasets/NAMAA-Space/Arabic-Reranking-Triplet-5-Eval)
 
-
+![Plot](https://huggingface.co/NAMAA-Space/Namaa-Reranker-v1/resolve/main/Dataset1_Evaluation.jpg)
 
 As seen, The model performs extremly well in comparison to other famous rerankers.