Hakim: Farsi Text Embedding Model

Mehran Sarmadi; Morteza Alikhani; Erfan Zinvandi; Zahra Pourbahman

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arXiv · 2505.08435

Hakim: Farsi Text Embedding Model

Mehran Sarmadi·Morteza Alikhani·Erfan Zinvandi·Zahra Pourbahman

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Abstract

Recent advancements in text embedding have significantly improved natural language understanding across many languages, yet Persian remains notably underrepresented in large-scale embedding research. In this paper, we present Hakim, a novel state-of-the-art Persian text embedding model that achieves a 8.5% performance improvement over existing approaches on the FaMTEB benchmark, outperforming all previously developed Persian language models. As part of this work, we introduce three new datasets - Corpesia, Pairsia-sup, and Pairsia-unsup - to support supervised and unsupervised training scenarios. Additionally, Hakim is designed for applications in chatbots and retrieval-augmented generation (RAG) systems, particularly addressing retrieval tasks that require incorporating message history within these systems. We also propose a new baseline model built on the BERT architecture. Our language model consistently achieves higher accuracy across various Persian NLP tasks, while the RetroMAE-based model proves particularly effective for textual information retrieval applications. Together, these contributions establish a new foundation for advancing Persian language understanding.

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BibTeX RIS

Mehran Sarmadi, Morteza Alikhani, Erfan Zinvandi, Zahra Pourbahman. 2025-05-13. Hakim: Farsi Text Embedding Model. https://arxiv.org/abs/2505.08435

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