---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:MultipleNegativesRankingLoss
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: apache-2.0
language:
- ar
base_model:
- aubmindlab/bert-base-arabertv02
---


### 🚀 Arabic-Retrieval-v1.0

This is a high-performance Arabic information retrieval 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.  

---

## 🔑 Key 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**. 

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity


### 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})
)
```

## Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
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 folllowing way:

```python
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}")
```

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## Evaluation
This model has been ealuated 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 is compared to other highly performant models:

| **model**                           |   **1**   |    **2**     |    **3**    |
|-------------------------------------|-----------|--------------|-------------|
| Arabic-Retrieval-v1.0               |   0.875   |   **0.72**   |     0.679   | 
| intfloat/multilingual-e5-large      |  **0.89** |     0.719    |   **0.698** |
| intfloat/multilingual-e5-base       |   0.87    |     0.69     |     0.686   |



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#### Sentence Transformers
```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
```bibtex
@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}
}
```

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