Arabic-Triplet-Matryoshka-V2-Model is a state-of-the-art Arabic language embedding model based on the sentence-transformers framework. It is fine-tuned from aubmindlab/bert-base-arabertv02 and specifically designed to capture the rich semantic nuances of Arabic text.
This model maps sentences and paragraphs to a 768-dimensional dense vector space, enabling high-quality semantic text operations including:
Semantic textual similarity
Semantic search
Paraphrase mining
Text classification
Clustering
Information retrieval
Question answering
Key Features
State-of-the-Art Performance: Achieved 0.85 on STS17 and 0.64 on STS22.v2 with an average score of 74.5, making it the leading Arabic embedding model currently available.
MatryoshkaLoss Training: Utilizes nested embedding learning techniques to create hierarchical embeddings at multiple resolutions.
Optimization: Trained for 3 epochs with a final training loss of 0.718.
Full Arabic Language Support: Designed specifically to handle the complexity and morphological richness of Arabic language.
Training Details
The model was trained using a combination of two loss functions:
MatryoshkaLoss: Enables the creation of nested embeddings at multiple resolutions, allowing for efficient and adaptable representations.
MultipleNegativesRankingLoss: Enhances the model's ability to discriminate between semantically similar and dissimilar text pairs.
The model demonstrates exceptional performance on standard Arabic semantic textual similarity benchmarks:
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
This represents the current state-of-the-art for Arabic embedding models, outperforming previous approaches by a significant margin.
Use Cases
This model is particularly well-suited for:
Information Retrieval: Enhancing search capabilities for Arabic content.
Document Similarity: Identifying similar documents or text passages.
Text Classification: Powering classification systems for Arabic content.
Question Answering: Supporting Arabic QA systems with improved semantic understanding.
Semantic Clustering: Organizing Arabic text data based on meaning.
Cross-lingual Applications: When combined with other language models for multilingual applications.
Usage Examples
python
1from sentence_transformers import SentenceTransformer
23# Download from the 🤗 Hub4model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2")5# Run inference6sentences =[7'SENTENCE 1',8'SENTENCE 2',9'SENTENCE 3',10]11embeddings = model.encode(sentences)12print(embeddings.shape)13# [3, 768]1415# Get the similarity scores for the embeddings16similarities = model.similarity(embeddings, embeddings)17print(similarities.shape)18# [3, 3]
Limitations
Despite its strong performance, users should be aware of the following limitations:
The model may not perform optimally on highly technical or domain-specific Arabic text that was underrepresented in the training data.
As with all embedding models, performance may vary across different Arabic dialects and regional variations.
The model is optimized for semantic similarity tasks and may require fine-tuning for other specific applications.
Ethical Considerations
This model is intended for research and applications that benefit Arabic language processing. Users should be mindful of potential biases that may exist in the training data and the resulting embeddings. We encourage responsible use of this technology and welcome feedback on ways to improve fairness and representation.
Citation
If you use the Arabic Matryoshka Embeddings Model in your research or applications, please cite it as follows:
bibtex
1@article{nacar2025gate,
2 title={GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training},
3 author={Nacar, Omer and Koubaa, Anis and Sibaee, Serry and Al-Habashi, Yasser and Ammar, Adel and Boulila, Wadii},
4 journal={arXiv preprint arXiv:2505.24581},
5 year={2025}
6}
Acknowledgements
We would like to acknowledge AraBERT for the base model and akhooli for the valuable dataset that made this work possible.
