README.md

metadata

base_model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
datasets:
  - Omartificial-Intelligence-Space/Arabic-stsb
  - Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class
language:
  - ar
library_name: sentence-transformers
license: apache-2.0
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: feature-extraction
tags:
  - mteb
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:947818
  - loss:SoftmaxLoss
  - loss:CosineSimilarityLoss
  - transformers
widget:
  - source_sentence: امرأة تكتب شيئاً
    sentences:
      - مراهق يتحدث إلى فتاة عبر كاميرا الإنترنت
      - امرأة تقطع البصل الأخضر.
      - مجموعة من كبار السن يتظاهرون حول طاولة الطعام.
  - source_sentence: تتشكل النجوم في مناطق تكوين النجوم، والتي تنشأ نفسها من السحب الجزيئية.
    sentences:
      - لاعب كرة السلة على وشك تسجيل نقاط لفريقه.
      - المقال التالي مأخوذ من نسختي من "أطلس البطريق الجديد للتاريخ الوسطى"
      - قد يكون من الممكن أن يوجد نظام شمسي مثل نظامنا خارج المجرة
  - source_sentence: >-
      تحت السماء الزرقاء مع الغيوم البيضاء، يصل طفل لمس مروحة طائرة واقفة على
      حقل من العشب.
    sentences:
      - امرأة تحمل كأساً
      - طفل يحاول لمس مروحة طائرة
      - اثنان من عازبين عن الشرب يستعدون للعشاء
  - source_sentence: رجل في منتصف العمر يحلق لحيته في غرفة ذات جدران بيضاء والتي لا تبدو كحمام
    sentences:
      - فتى يخطط اسمه على مكتبه
      - رجل ينام
      - المرأة وحدها وهي نائمة في غرفة نومها
  - source_sentence: الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.
    sentences:
      - شخص طويل القامة
      - المرأة تنظر من النافذة.
      - لقد مات الكلب
model-index:
  - name: Omartificial-Intelligence-Space/GATE-AraBert-v1
    results:
      - task:
          type: STS
        dataset:
          name: MTEB STS17 (ar-ar)
          type: mteb/sts17-crosslingual-sts
          config: ar-ar
          split: test
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
        metrics:
          - type: cosine_pearson
            value: 82.06597171670848
          - type: cosine_spearman
            value: 82.7809395809498
          - type: euclidean_pearson
            value: 79.23996991139896
          - type: euclidean_spearman
            value: 81.5287595404711
          - type: main_score
            value: 82.7809395809498
          - type: manhattan_pearson
            value: 78.95407006608013
          - type: manhattan_spearman
            value: 81.15109493737467
      - task:
          type: STS
        dataset:
          name: MTEB STS22.v2 (ar)
          type: mteb/sts22-crosslingual-sts
          config: ar
          split: test
          revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
        metrics:
          - type: cosine_pearson
            value: 54.912880452465004
          - type: cosine_spearman
            value: 63.09788380910325
          - type: euclidean_pearson
            value: 57.92665617677832
          - type: euclidean_spearman
            value: 62.76032598469037
          - type: main_score
            value: 63.09788380910325
          - type: manhattan_pearson
            value: 58.0736648155273
          - type: manhattan_spearman
            value: 62.94190582776664
      - task:
          type: STS
        dataset:
          name: MTEB STS22 (ar)
          type: mteb/sts22-crosslingual-sts
          config: ar
          split: test
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
        metrics:
          - type: cosine_pearson
            value: 51.72534929358701
          - type: cosine_spearman
            value: 59.75149627160101
          - type: euclidean_pearson
            value: 53.894835373598774
          - type: euclidean_spearman
            value: 59.44278354697161
          - type: main_score
            value: 59.75149627160101
          - type: manhattan_pearson
            value: 54.076675975406985
          - type: manhattan_spearman
            value: 59.610061143235725

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
• 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

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.

## 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}
}