Omartificial-Intelligence-Space/GATE-AraBert-v1

GATE-AraBert-V1

This is GATE | General Arabic Text Embedding trained using SentenceTransformers in a multi-task setup. The system trains on the AllNLI and on the STS dataset. It is described in detail in the paper GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Hybrid Loss Training.

Project page: https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2 <!-- at revision 5ce4f80f3ede26de623d6ac10681399dba5c684a -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Cosine Similarity
• —Training Datasets:
• —all-nli
• —sts
• —Language: ar

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/GATE-AraBert-v1")
# Run inference
sentences = [
    'الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.',
    'لقد مات الكلب',
    'شخص طويل القامة',
]
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

<span style="color:blue">Acknowledgments</span>

The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.

## Citation

If you use the GATE, please cite it as follows:

@article{nacar2025gate,
  title={GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training},
  author={Nacar, Omer and Koubaa, Anis and Sibaee, Serry and Al-Habashi, Yasser and Ammar, Adel and Boulila, Wadii},
  journal={arXiv preprint arXiv:2505.24581},
  year={2025}
}