Omartificial-Intelligence-Space/AraEuroBert-610M

Ara-EuroBERT: Arabic Semantic Text Embeddings

Ara-EuroBERT-610M is a sentence-transformers model fine-tuned from EuroBERT/EuroBERT-610m specifically optimized for Semantic Arabic text embeddings.

This model maps sentences and paragraphs to a 1152-dimensional dense vector space and Maximum Sequence Length: 8,192 tokens.

It can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

You can find more information on the base model at https://huggingface.co/EuroBERT/EuroBERT-610m

Model Details & Benchmark Performance

The benchmark results above demonstrate the significant performance improvements of AraEuroBERT models compared to standard EuroBERT models:

• STS17 Benchmark: AraEuroBERT-610M achieves a score of 83, significantly outperforming the standard EuroBERT-610M (14) and even the much larger EuroBERT-2.1B (12).
• STS22.v2 Benchmark: AraEuroBERT-210M scores 61, outperforming both the larger AraEuroBERT-610M (53) and all standard EuroBERT variants.

These results highlight the effectiveness of our specialized fine-tuning for Arabic text embeddings, with even our smaller 210M parameter model demonstrating superior performance on Arabic semantic tasks.

Metrics

Semantic Similarity

• Datasets: sts-dev-1152, sts-dev-960, sts-dev-768 and sts-dev-512
• Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev-1152	sts-dev-960	sts-dev-768	sts-dev-512
pearson_cosine	0.8264	0.8259	0.8244	0.8238
spearman_cosine	0.8307	0.8302	0.8293	0.8293

Model Description

• Model Type: Sentence Transformer
• Base model: EuroBERT/EuroBERT-610m
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1152 dimensions (supports nested dimensions: 1152, 960, 768, 512)
• Similarity Function: Cosine Similarity
• Training Dataset: 2.28M training samples with Arabic text triplets
• Language: Arabic

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: EuroBertModel 
  (1): Pooling({'word_embedding_dimension': 1152, '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:

pip install -U sentence-transformers

Then you can load this model and run inference:

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Omartificial-Intelligence-Space/AraEuroBert-610M")

# Run inference
sentences = [
    'لاعبة كرة ناعمة ترمي الكرة إلى زميلتها في الفريق',
    'شخصان يلعبان كرة البيسبول',
    'لاعبين لكرة البيسبول يجلسان على مقعد',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1152]

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

Nested Embeddings

This model supports Matryoshka (nested) embeddings with the following dimensions:

• Full dimension: 1152
• Reduced dimensions: 960, 768, 512

You can select the embedding dimension based on your specific requirements, trading off between performance and computational efficiency.

Citation

If you use this model in your research, please cite the following works:

@misc{boizard2025eurobertscalingmultilingualencoders,
      title={EuroBERT: Scaling Multilingual Encoders for European Languages}, 
      author={Nicolas Boizard and Hippolyte Gisserot-Boukhlef and Duarte M. Alves and André Martins and Ayoub Hammal and Caio Corro and Céline Hudelot and Emmanuel Malherbe and Etienne Malaboeuf and Fanny Jourdan and Gabriel Hautreux and João Alves and Kevin El-Haddad and Manuel Faysse and Maxime Peyrard and Nuno M. Guerreiro and Patrick Fernandes and Ricardo Rei and Pierre Colombo},
      year={2025},
      eprint={2503.05500},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.05500}, 
}

@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",
}

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}