GATE-Reranker-V1 Open-Source Model - Specially Designed for Arabic Document Retrieval and Ranking, with Superior Performance!

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GATE Reranker V1

Developed by NAMAA-Space

A high-performance re-ranking model optimized for Arabic document retrieval and sorting

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Transformers

ArabicOpen Source License:Apache-2.0 #Arabic Re-ranking #Retrieval-Augmented Generation #High-Precision Relevance

Downloads 615

Release Time : 10/25/2024

Model Overview

A specialized Arabic-optimized model based on GATE-AraBert-v1, enhancing search relevance ranking by accurately assessing the contextual match between documents and queries

Model Features

Arabic-Specific Optimization

Built on the high-performance GATE-AraBert-v1 model, trained with rich Arabic corpus data

Advanced Document Ranking

Precisely ranks search results, perfectly suited for search engines, recommendation systems, and question-answering applications

Top-Tier Performance

Demonstrates superior performance compared to well-known re-ranking models, ensuring reliable relevance and accuracy

Model Capabilities

Arabic Document Retrieval

Search Result Re-ranking

Query-Document Relevance Assessment

Use Cases

Information Retrieval

Retrieval-Augmented Generation

Enhances the relevance of search results for Arabic content

Significantly improves the quality and relevance of generated content

Recommendation Systems

Content Recommendation

Provides high-quality Arabic content recommendations

Increases user satisfaction and engagement

Question Answering Systems

Intelligent Q&A

Enhances the answer retrieval quality for Arabic question-answering systems

Delivers more accurate and relevant answers

🚀 GATE-Reranker-V1

NAMAA-space releases GATE-Reranker-V1, a high - performance model fine - tuned to elevate Arabic document retrieval and ranking to new heights! This model is designed to improve the search relevance of Arabic documents by accurately ranking them based on their contextual fit for a given query.

🚀 Quick Start

The model is ready to be used for Arabic document retrieval and ranking tasks right away. Just follow the usage example below to get started.

✨ Features

Optimized for Arabic: Built on the highly performant Omartificial-Intelligence-Space/GATE-AraBert-v1 with exclusively rich Arabic data.
Advanced Document Ranking: Ranks results with precision, perfect for search engines, recommendation systems, and question - answering applications.
State - of - the - Art Performance: Achieves excellent performance compared to famous rerankers (See Evaluation), ensuring reliable relevance and precision.

💼 Example Use Cases

Retrieval Augmented Generation: Improve search result relevance for Arabic content.
Content Recommendation: Deliver top - tier Arabic content suggestions.
Question Answering: Boost answer retrieval quality in Arabic - focused systems.

💻 Usage Examples

Basic Usage

The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre - trained models like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('NAMAA-Space/GATE-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'
Paragraph2 = 'الذكاء الاصطناعي يستخدم في تحسين الإنتاجية في الصناعات'

scores = model.predict([(Query, Paragraph1), (Query, Paragraph2)])

📚 Documentation

Evaluation

We evaluate our model on two different datasets using the metrics MAP, MRR and NDCG@10:

The purpose of this evaluation is to highlight the performance of our model with regards to: Relevant/Irrelevant labels and positive/multiple negatives documents:

Dataset 1: NAMAA-Space/Ar-Reranking-Eval

Plot

Dataset 2: NAMAA-Space/Arabic-Reranking-Triplet-5-Eval

Plot

As seen, the model performs extremely well in comparison to other famous rerankers.

WIP: More comparisons and evaluation on Arabic datasets.

📄 License

This project is licensed under the Apache 2.0 license.

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