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ARA-Reranker-V1

Omartificial-Intelligence-Space

✨ This model is designed specifically for Arabic language reranking tasks, optimized to handle queries and passages with precision. ✨ Unlike embedding models, which generate vector representations, this reranker directly evaluates the similarity between a question and a document, outputting a relevance score. ✨ Trained on a combination of positive and hard negative query-passage pairs, it excels in identifying the most relevant results.

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Arabic-Triplet-Matryoshka-V2

Omartificial-Intelligence-Space

ATM2 is a **135M-parameter Arabic embedding model** fine-tuned from AraBERTv02 using the Sentence Transformers framework. It maps Arabic sentences and passages into **768-dimensional vectors** for comparing meaning and retrieving related content. ATM2 produces embeddings, not generated answers. Classification requires an additional classifier or decision rule; question answering requires downstream retrieval and answer-generation components. For larger collections, precompute passage embeddings and store them in a vector index. Split long documents into passages within the effective sequence limit. Similarity scores indicate relative semantic proximity; they are not calibrated probabilities of correctness.

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GATE-AraBert-v1

Omartificial-Intelligence-Space

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 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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