sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer loss:MultipleNegativesRankingLoss mteb ar arxiv:1908.10084 arxiv:1705.00652 base_model:NAMAA-Space/AraModernBert-Base-V1.0 base_model:finetune:NAMAA-Space/AraModernBert-Base-V1.0 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: NAMAA-Space/AraModernBert-Base-V1.0
language:
- ar
library_name: sentence-transformers
license: apache-2.0
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:MultipleNegativesRankingLoss
- mteb
widget:
- source_sentence: الذكاء الاصطناعي يغير طريقة تفاعلنا مع التكنولوجيا.
  sentences:
  - التكنولوجيا تتطور بسرعة بفضل الذكاء الاصطناعي.
  - الذكاء الاصطناعي يسهم في تطوير التطبيقات الذكية.
  - تحديات الذكاء الاصطناعي تشمل الحفاظ على الأمان والأخلاقيات.
model-index:
- name: NAMAA-Space/AraModernBert-Base-V1.0
  results:
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (ar-ar)
      type: mteb/sts17-crosslingual-sts
      config: ar-ar
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 82.4888
    - type: spearman
      value: 83.0981
    - type: cosine_pearson
      value: 82.4888
    - type: cosine_spearman
      value: 83.1109
    - type: manhattan_pearson
      value: 81.2051
    - type: manhattan_spearman
      value: 83.0197
    - type: euclidean_pearson
      value: 81.1013
    - type: euclidean_spearman
      value: 82.8922
    - type: main_score
      value: 83.1109
  - task:
      type: STS
    dataset:
      name: MTEB STS22.v2 (ar)
      type: mteb/sts22-crosslingual-sts
      config: ar
      split: test
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
    metrics:
    - type: pearson
      value: 52.58540000000001
    - type: spearman
      value: 61.7371
    - type: cosine_pearson
      value: 52.58540000000001
    - type: cosine_spearman
      value: 61.7371
    - type: manhattan_pearson
      value: 55.887299999999996
    - type: manhattan_spearman
      value: 61.3654
    - type: euclidean_pearson
      value: 55.633500000000005
    - type: euclidean_spearman
      value: 61.2124
    - type: main_score
      value: 61.7371
---

# SentenceTransformer based on NAMAA-Space/AraModernBert-Base-V1.0

This SentenceTransformer is fine-tuned from [NAMAA-Space/AraModernBert-Base-V1.0](https://huggingface.co/NAMAA-Space/AraModernBert-Base-V1.0), bringing strong arabic embeddings useful for a multiple of use cases.

🔹 **768-dimensional dense vectors** 🎯  
🔹 **Excels in**: Semantic Similarity, Search, Paraphrase Mining, Clustering, Text Classification & More!  
🔹 **Optimized for speed & efficiency** without sacrificing performance  

Whether you're building intelligent search engines, chatbots, or AI-powered knowledge graphs, this model delivers meaningful representations of Arabic text with precision and depth.

Try it out & bring Arabic NLP to the next level! 🔥✨

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("NAMAA-Space/AraModernBert-Base-STS")
# Run inference
sentences = [
    'الذكاء الاصطناعي يغير طريقة تفاعلنا مع التكنولوجيا.',
    'التكنولوجيا تتطور بسرعة بفضل الذكاء الاصطناعي.',
    'الذكاء الاصطناعي يسهم في تطوير التطبيقات الذكية.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

## Evaluation

### Metrics

#### Semantic Similarity

* Datasets: ` STS17` and `STS22.v2`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | STS17     | STS22.v2   |
|:--------------------|:----------|:-----------|
| pearson_cosine      | 0.8249    | 0.5259     |
| **spearman_cosine** | **0.831** | **0.6169** |

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### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.1.0+cu118
- Accelerate: 1.4.0
- Datasets: 2.21.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### 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}
}
```