GATE-AraBert-V1

Name: GATE-AraBert-v1
Availability: InStock

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
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.

python

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

Model	Dim	# Params.	STS17	STS22-v2	Average
Arabic-Triplet-Matryoshka-V2	768	135M	85	64	75
Arabert-all-nli-triplet-Matryoshka	768	135M	83	64	74
AraGemma-Embedding-300m	768	303M	84	62	73
GATE-AraBert-V1	767	135M	83	63	73
Marbert-all-nli-triplet-Matryoshka	768	163M	82	61	72
Arabic-labse-Matryoshka	768	471M	82	61	72
AraEuroBert-Small	768	210M	80	61	71
E5-all-nli-triplet-Matryoshka	384	278M	80	60	70
text-embedding-3-large	3072	-	81	59	70
Arabic-all-nli-triplet-Matryoshka	768	135M	82	54	68
AraEuroBert-Mid	1151	610M	83	53	68
paraphrase-multilingual-mpnet-base-v2	768	135M	79	55	67
AraEuroBert-Large	2304	2.1B	79	55	67
text-embedding-ada-002	1536	-	71	62	66
text-embedding-3-small	1536	-	72	57	65

Acknowledgments

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.

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## 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}
}