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README.md

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+# Arabic Reranker Model
 
+This is an Arabic reranker model, fine-tuned from the [Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2](https://huggingface.co/Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2), which itself is based on [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02). The model is designed to perform reranking tasks by scoring and ordering text options based on their relevance to a given query, specifically optimized for Arabic text.
+
+This model was trained on a synthetic dataset of Arabic triplets generated using large language models (LLMs). It was refined using a scoring technique, making it ideal for ranking tasks in Arabic Natural Language Processing (NLP).
+
+## Model Use
+
+This model is well-suited for Arabic text reranking tasks, including:
+- Information retrieval and document ranking
+- Search engine results reranking
+- Question-answering tasks requiring ranked answer choices
+
+## Example Usage
+
+Below is an example of how to use the model with the `sentence_transformers` library to rerank paragraphs based on relevance to a query.
+
+### Code Example
+
+```python
 from sentence_transformers import CrossEncoder
+
+# Load the model
 model = CrossEncoder('oddadmix/arabic-reranker', max_length=512)
 
+# Define the query and candidate paragraphs
 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'
 
+# Score the paragraphs based on relevance to the query
 scores = model.predict([(Query, Paragraph1), (Query, Paragraph2)])
 
-```
+# Output scores
+print("Score for Paragraph 1:", scores[0])
+print("Score for Paragraph 2:", scores[1])