update readme.md

README.md

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 **For more info please refer to this blog: [ARM | Arabic Reranker Model](www.omarai.me).**
 
 ✨ This model is designed specifically for Arabic language reranking tasks, optimized to handle queries and passages with precision. 
-✨ Unlike embedding models, which generate vector representations, this reranker directly evaluates the similarity between a question and a document, outputting a relevance score. 
+
+✨ Unlike embedding models, which generate vector representations, this reranker directly evaluates the similarity between a question and a document, outputting a relevance score.
+
 ✨ Trained on a combination of positive and hard negative query-passage pairs, it excels in identifying the most relevant results. 
+
 ✨ The output score can be transformed into a [0, 1] range using a sigmoid function, providing a clear and interpretable measure of relevance.
 
 
 
+## Usage 
+### Using sentence-transformers
+
+```
+pip installsentence-transformers
+```
+```python
+from sentence_transformers import CrossEncoder
+
+# Load the cross-encoder model
+
+# Define a query and a set of candidates with varying degrees of relevance
+query = "تطبيقات الذكاء الاصطناعي تُستخدم في مختلف المجالات لتحسين الكفاءة."
+
+# Candidates with varying relevance to the query
+candidates = [
+    "الذكاء الاصطناعي يساهم في تحسين الإنتاجية في الصناعات المختلفة.", # Highly relevant
+    "نماذج التعلم الآلي يمكنها التعرف على الأنماط في مجموعات البيانات الكبيرة.", # Moderately relevant
+    "الذكاء الاصطناعي يساعد الأطباء في تحليل الصور الطبية بشكل أفضل.", # Somewhat relevant
+    "تستخدم الحيوانات التمويه كوسيلة للهروب من الحيوانات المفترسة.", # Irrelevant
+]
+
+# Create pairs of (query, candidate) for each candidate
+query_candidate_pairs = [(query, candidate) for candidate in candidates]
+
+# Get relevance scores from the model
+scores = model.predict(query_candidate_pairs)
+
+# Combine candidates with their scores and sort them by score in descending order (higher score = higher relevance)
+ranked_candidates = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
+
+# Output the ranked candidates with their scores
+print("Ranked candidates based on relevance to the query:")
+for i, (candidate, score) in enumerate(ranked_candidates, 1):
+    print(f"Rank {i}:")
+    print(f"Candidate: {candidate}")
+    print(f"Score: {score}\n")
+```
+## Evaluation 
+
+
+
+
+## <span style="color:blue">Acknowledgments</span>
+
+The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.
+
+
+```markdown
+## Citation
+
+If you use the GATE, please cite it as follows:
+
+@misc{nacar2025ARM,
+      title={ARM, Arabic Reranker Model}, 
+      author={Omer Nacar},
+      year={2025},
+      url={https://huggingface.co/Omartificial-Intelligence-Space/ARA-Reranker-V1},
+}
+
+