adelpro/atm-v2-web

Arabic Triplet Matryoshka V2 (ATM-V2) — INT8 ONNX (Web / transformers.js)

This is an INT8 dynamic-quantized ONNX export of Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2, the state-of-the-art Arabic embedding model (ATM2), laid out for the transformers.js (@huggingface/transformers) web runtime. It powers the "بالذكاء الاصطناعي" (AI) semantic-search tab of the open-mushaf Quran reader in the browser (PWA).

The model maps Arabic sentences and verses to a 768-dimensional dense vector space for semantic search, similarity, IR, and clustering. It is fine-tuned from aubmindlab/bert-base-arabertv02 and described in the paper GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Hybrid Loss Training.

Model Description

Arabic-Triplet-Matryoshka-V2-Model is a state-of-the-art Arabic language embedding model based on the sentence-transformers framework and specifically designed to capture the rich semantic nuances of Arabic text.

Attribute	Value
Base model	aubmindlab/bert-base-arabertv02
Embedding dimension	768
Parameters	135M
Pretrained model format	fp32 safetensors
This export	INT8 dynamic-quantized ONNX (onnx/model_quantized.onnx)
Runtime	@huggingface/transformers (transformers.js) WASM backend
License	Apache-2.0

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 on Arabic semantic similarity.
• MatryoshkaLoss Training: Nested embeddings at multiple resolutions.
• Full Arabic Support: Handles the complexity and morphological richness of Arabic.
• Browser-ready: runs in-browser via transformers.js with the q8 dtype.

Training Details

• Loss functions: MatryoshkaLoss + MultipleNegativesRankingLoss.
• Dataset: akhooli/arabic-triplets-1m-curated-sims-len (1M samples).
• Epochs: 3 · Final loss: 0.718 · Embedding dim: 768.

Performance

From the original model card (Arabic semantic similarity benchmarks):

Model	Dim	Params	STS17	STS22-v2	Avg
ATM-V2 (this repo base)	768	135M	85	64	75

See the upstream Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2 card for the full comparison table.

Use Cases

• Semantic textual similarity, document retrieval, question answering, clustering, and classification of Arabic text.
• Client-side semantic search in the browser (as used by the open-mushaf PWA).

File layout (transformers.js)

This repo hosts the web layout expected by transformers.js:

config.json
tokenizer.json
tokenizer_config.json
special_tokens_map.json
onnx/model_quantized.onnx

With the pipeline configured as dtype: 'q8', transformers.js resolves the q8 precision to onnx/model_quantized.onnx. The repo must remain public — transformers.js fetches these files over plain HTTPS with no auth.

A companion native layout lives in adelpro/atm-v2-int8-onnx.

Usage (@huggingface/transformers)

import { pipeline } from '@huggingface/transformers';

const extractor = await pipeline('feature-extraction', 'adelpro/atm-v2-web', {
  dtype: 'q8',
  device: 'wasm',
});

const out = await extractor('الرَّحْمَنُ عَلَّمَ الْقُرْآنَ', {
  pooling: 'mean',
  normalize: true,
});
// out.data is the 768-dim L2-normalized embedding.

Note: the vector index built for this model uses mean pooling + L2 normalization with no query:/passage: prefixes (the upstream model card shows plain encode(), not an E5-style prefix convention). Keep query and stored passages in the same unprefixed space. The WASM backend runs single-threaded to avoid SharedArrayBuffer / cross-origin isolation requirements.

Limitations

• May not perform optimally on highly technical / domain-specific Arabic that was underrepresented in training.
• A one-word short query (e.g. a proper noun) embeds weakly relative to long passages; semantically-linked epithets may not surface at high cosine.
• Int8 dynamic quantization introduces small weight-precision loss compared to the fp32 original.

Ethical Considerations

Intended for research and applications that benefit Arabic language processing. Biases that may exist in the training data should be considered.

Citation

Cite the upstream paper when referencing this model's methodology:

@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}
}

Acknowledgements

Built on aubmindlab/bert-base-arabertv02 and the akhooli/arabic-triplets-1m-curated-sims-len dataset.

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Derived from Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2 (Apache-2.0). This ONNX export is the browser runtime model for the open-mushaf-native Quran reader.