Introducing ARM-V1 | Arabic Reranker Model (Version 1)
For more info please refer to this blog: ARM | Arabic Reranker Model.
✨ 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.
✨ 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.
Arabic RAG Pipeline
Usage
Using sentence-transformers
pip install sentence-transformers
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
Dataset
Size: 3000 samples.
Structure:
🔸 Query: A string representing the user's question.
🔸 Candidate Document: A candidate passage to answer the query.
🔸 Relevance Label: Binary label (1 for relevant, 0 for irrelevant).
Evaluation Process
🔸 Query Grouping: Queries are grouped to evaluate the model's ability to rank candidate documents correctly for each query.
🔸 Model Prediction: Each model predicts relevance scores for all candidate documents corresponding to a query.
🔸 Metrics Calculation: Metrics are computed to measure how well the model ranks relevant documents higher than irrelevant ones.
| Model | MRR | MAP | nDCG@10 |
|---|---|---|---|
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 0.631 | 0.6313 | 0.725 |
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 0.664 | 0.664 | 0.750 |
| BAAI/bge-reranker-v2-m3 | 0.902 | 0.902 | 0.927 |
| Omartificial-Intelligence-Space/ARA-Reranker-V1 | 0.934 | 0.9335 | 0.951 |
Acknowledgments
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.
## 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},
}