Arabic Reranker

🚀 Arabic Reranker Model

This is an Arabic reranker model that scores and orders text options for reranking tasks, specifically optimized for Arabic text.

📦 Model Information

This Arabic reranker model is fine - tuned from the Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2, which itself is based on aubmindlab/bert-base-arabertv02. It is designed to perform reranking tasks by scoring and ordering text options according to their relevance to a given query, with specific optimization for Arabic text.

The model was trained on a synthetic dataset of Arabic triplets generated by large language models (LLMs). It was refined using a scoring technique, making it a great fit for ranking tasks in Arabic Natural Language Processing (NLP).

🚀 Quick Start

✨ Features

This model is highly suitable for Arabic text reranking tasks, including:

• Information retrieval and document ranking
• Search engine results reranking
• Question - answering tasks that require ranked answer choices

💻 Usage Examples

Basic Usage

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])