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NAMAA-Space/AraModernBert-Base-STS

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
6likes243downloads
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SentenceTransformer based on NAMAA-Space/AraModernBert-Base-V1.0

This SentenceTransformer is fine-tuned from 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
MetricSTS17STS22.v2
pearson_cosine0.82490.5259
spearman_cosine0.8310.6169

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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}
}