README.md

---
base_model:
- aubmindlab/bert-base-arabertv02
language:
- ar
model-index:
- name: omarelshehy/Arabic-Retrieval-v1.0
  results:
  - dataset:
      config: ar
      name: MTEB MIRACLRetrieval (ar)
      revision: main
      split: dev
      type: miracl/mmteb-miracl
    metrics:
    - type: main_score
      value: 58.664
    - type: map_at_1
      value: 32.399
    - type: map_at_10
      value: 50.236000000000004
    - type: map_at_100
      value: 51.87199999999999
    - type: map_at_1000
      value: 51.926
    - type: ndcg_at_1
      value: 48.376999999999995
    - type: ndcg_at_10
      value: 58.664
    - type: ndcg_at_100
      value: 63.754999999999995
    - type: ndcg_at_1000
      value: 64.672
    - type: ndcg_at_20
      value: 61.111000000000004
    - type: ndcg_at_3
      value: 51.266
    - type: ndcg_at_5
      value: 54.529
    task:
      type: Retrieval
  - dataset:
      config: ar
      name: MTEB MIRACLRetrievalHardNegatives (ar)
      revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
      split: dev
      type: mteb/miracl-hard-negatives
    metrics:
    - type: main_score
      value: 60.026
    - type: map_at_1
      value: 32.547
    - type: map_at_10
      value: 51.345
    - type: map_at_100
      value: 53.190000000000005
    - type: map_at_1000
      value: 53.237
    - type: ndcg_at_1
      value: 48.3
    - type: ndcg_at_10
      value: 60.026
    - type: ndcg_at_100
      value: 65.62400000000001
    - type: ndcg_at_1000
      value: 66.282
    - type: ndcg_at_20
      value: 62.856
    - type: ndcg_at_3
      value: 52.1
    - type: ndcg_at_5
      value: 55.627
    task:
      type: Retrieval
  - dataset:
      config: ara-ara
      name: MTEB MLQARetrieval (ara-ara)
      revision: 397ed406c1a7902140303e7faf60fff35b58d285
      split: test
      type: facebook/mlqa
    metrics:
    - type: main_score
      value: 56.032000000000004
    - type: map_at_1
      value: 45.218
    - type: map_at_10
      value: 52.32599999999999
    - type: map_at_100
      value: 53.001
    - type: map_at_1000
      value: 53.047999999999995
    - type: ndcg_at_1
      value: 45.228
    - type: ndcg_at_10
      value: 56.032000000000004
    - type: ndcg_at_100
      value: 59.486000000000004
    - type: ndcg_at_1000
      value: 60.938
    - type: ndcg_at_20
      value: 57.507
    - type: ndcg_at_3
      value: 52.05800000000001
    - type: ndcg_at_5
      value: 54.005
    task:
      type: Retrieval
  - dataset:
      config: ara-ara
      name: MTEB MLQARetrieval (ara-ara)
      revision: 397ed406c1a7902140303e7faf60fff35b58d285
      split: validation
      type: facebook/mlqa
    metrics:
    - type: main_score
      value: 71.11
    - type: map_at_1
      value: 58.221000000000004
    - type: map_at_10
      value: 67.089
    - type: map_at_100
      value: 67.62700000000001
    - type: map_at_1000
      value: 67.648
    - type: ndcg_at_1
      value: 58.221000000000004
    - type: ndcg_at_10
      value: 71.11
    - type: ndcg_at_100
      value: 73.824
    - type: ndcg_at_1000
      value: 74.292
    - type: ndcg_at_20
      value: 72.381
    - type: ndcg_at_3
      value: 67.472
    - type: ndcg_at_5
      value: 69.803
    task:
      type: Retrieval
  - dataset:
      config: ar
      name: MTEB MintakaRetrieval (ar)
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
      split: test
      type: jinaai/mintakaqa
    metrics:
    - type: main_score
      value: 22.778000000000002
    - type: map_at_1
      value: 13.345
    - type: map_at_10
      value: 19.336000000000002
    - type: map_at_100
      value: 20.116999999999997
    - type: map_at_1000
      value: 20.246
    - type: ndcg_at_1
      value: 13.345
    - type: ndcg_at_10
      value: 22.778000000000002
    - type: ndcg_at_100
      value: 26.997
    - type: ndcg_at_1000
      value: 31.564999999999998
    - type: ndcg_at_20
      value: 24.368000000000002
    - type: ndcg_at_3
      value: 18.622
    - type: ndcg_at_5
      value: 20.72
    task:
      type: Retrieval
  - dataset:
      config: arabic
      name: MTEB MrTidyRetrieval (arabic)
      revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
      split: test
      type: mteb/mrtidy
    metrics:
    - type: main_score
      value: 55.584999999999994
    - type: map_at_1
      value: 34.197
    - type: map_at_10
      value: 48.658
    - type: map_at_100
      value: 49.491
    - type: map_at_1000
      value: 49.518
    - type: ndcg_at_1
      value: 36.91
    - type: ndcg_at_10
      value: 55.584999999999994
    - type: ndcg_at_100
      value: 59.082
    - type: ndcg_at_1000
      value: 59.711000000000006
    - type: ndcg_at_20
      value: 57.537000000000006
    - type: ndcg_at_3
      value: 48.732
    - type: ndcg_at_5
      value: 52.834
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SadeemQuestionRetrieval (default)
      revision: 3cb0752b182e5d5d740df547748b06663c8e0bd9
      split: test
      type: sadeem-ai/sadeem-ar-eval-retrieval-questions
    metrics:
    - type: main_score
      value: 67.916
    - type: map_at_1
      value: 31.785999999999998
    - type: map_at_10
      value: 58.18600000000001
    - type: map_at_100
      value: 58.287
    - type: map_at_1000
      value: 58.29
    - type: ndcg_at_1
      value: 31.785999999999998
    - type: ndcg_at_10
      value: 67.916
    - type: ndcg_at_100
      value: 68.44200000000001
    - type: ndcg_at_1000
      value: 68.53399999999999
    - type: ndcg_at_20
      value: 68.11
    - type: ndcg_at_3
      value: 66.583
    - type: ndcg_at_5
      value: 67.5
    task:
      type: Retrieval
  - dataset:
      config: ara-ara
      name: MTEB XPQARetrieval (ara-ara)
      revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
      split: test
      type: jinaai/xpqa
    metrics:
    - type: main_score
      value: 43.622
    - type: map_at_1
      value: 19.236
    - type: map_at_10
      value: 37.047000000000004
    - type: map_at_100
      value: 38.948
    - type: map_at_1000
      value: 39.054
    - type: ndcg_at_1
      value: 35.333
    - type: ndcg_at_10
      value: 43.622
    - type: ndcg_at_100
      value: 50.761
    - type: ndcg_at_1000
      value: 52.932
    - type: ndcg_at_20
      value: 46.686
    - type: ndcg_at_3
      value: 37.482
    - type: ndcg_at_5
      value: 39.635999999999996
    task:
      type: Retrieval
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:MultipleNegativesRankingLoss
- retrieval
- mteb
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: apache-2.0
---


### 🚀 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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## Citation

### BibTeX

```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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