Arabic Reranker V1

🚀 Arabic Reranker V1 Model

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

This is an Arabic reranker model, fine-tuned from the Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2, which itself is based on 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).

🚀 Quick Start

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

💻 Usage Examples

Basic 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.

from sentence_transformers import CrossEncoder

# Load the model
model = CrossEncoder('oddadmix/arabic-reranker-v1', 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])

📚 Documentation