Arabic Retrieval V1.0

🚀 Arabic-Retrieval-v1.0

This is a high - performance Arabic information retrieval model built using the robust sentence - transformers framework. It delivers state - of - the - art performance and is tailored to the richness and complexity of the Arabic language.

🚀 Quick Start

First, install the Sentence Transformers library:

pip install -U sentence-transformers

Then, you can load this model and run inference. It is important to add the prefixes <query>: and <passage>: to your queries and passages while retrieving in the following way:

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("omarelshehy/Arabic-Retrieval-v1.0")

# Query 
query = "<query>: كيف يمكن للذكاء الاصطناعي تحسين طرق التدريس التقليدية؟"

# Passages
passages = [
    "<passage>: طرق التدريس التقليدية تستفيد من الذكاء الاصطناعي عبر تحسين عملية المتابعة وتخصيص التجربة التعليمية. يقوم الذكاء الاصطناعي بتحليل بيانات الطلاب وتقديم توصيات فعالة للمعلمين حول طرق التدريس الأفضل.",
    "<passage>: تطوير التعليم الشخصي يعتمد بشكل كبير على الذكاء الاصطناعي، الذي يقوم بمتابعة تقدم الطلاب بشكل فردي. يقدم الذكاء الاصطناعي حلولاً تعليمية مخصصة لكل طالب بناءً على مستواه وأدائه.",
    "<passage>: الدقة في تقييم الطلاب تتزايد بفضل الذكاء الاصطناعي الذي يقارن النتائج مع معايير متقدمة. بالرغم من التحديات التقليدية، الذكاء الاصطناعي يوفر أدوات تحليل تتيح تقييماً أدق لأداء الطلاب."
]

# Encode query and passages 
embeddings_query = model.encode(queries)
embeddings_passages = model.encode(passages)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings_query, embeddings_passages)

# Get best matching passage to query
best_match = passages[similarities.argmax().item()]
print(f"Best matching passage is {best_match}")

✨ Features

• 🔥 Outstanding Performance: Matches the accuracy of top - tier multilingual models like e5 - multilingual - large. See [evaluation](https://huggingface.co/omarelshehy/Arabic - retrieval - v1.0#evaluation)
• 💡 Arabic - Focused: Designed specifically for the nuances and dialects of Arabic, ensuring more accurate and context - aware results.
• 📉 Lightweight Efficiency: Requires 25% - 50% less memory, making it ideal for environments with limited resources or edge deployments.

🌍 Why This Model?

Multilingual models are powerful, but they’re often bulky and not optimized for specific languages. This model bridges that gap, offering Arabic - native capabilities without sacrificing performance or efficiency. Whether you’re working on search engines, chatbots, or large - scale NLP pipelines, this model provides a fast, accurate, and resource - efficient solution.

📚 Documentation

Model Details

Model Description

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Evaluation

This model has been evaluated using 3 different datasets and the NDCG@10 metric:

• Dataset 1: [castorini/mr - tydi](https://huggingface.co/datasets/castorini/mr - tydi)
• Dataset 2: [Omartificial - Intelligence - Space/Arabic - finanical - rag - embedding - dataset](https://huggingface.co/datasets/Omartificial - Intelligence - Space/Arabic - finanical - rag - embedding - dataset)
• Dataset 3: [sadeem - ai/sadeem - ar - eval - retrieval - questions](https://huggingface.co/datasets/sadeem - ai/sadeem - ar - eval - retrieval - questions)

And it is compared to other highly performant models:

📄 License

This model is licensed under the apache - 2.0 license.

🔧 Technical Details

Citation

BibTeX

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al - Rfou and Brian Strope and Yun - hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}