@inproceedings{zamaninejad-etal-2025-tooka,
    title = "Tooka-{SBERT}: Lightweight Sentence Embedding models for {P}ersian",
    author = "Zamaninejad, Ghazal  and
      SadraeiJavaheri, MohammadAli  and
      Aghababaloo, Farnaz  and
      Rafiee, Hamideh  and
      Oskuee, Milad Molazadeh  and
      Salehoof, AmirMohammad",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.147/",
    pages = "2415--2425",
    ISBN = "979-8-89176-303-6",
    abstract = "We introduce Tooka-SBERT, a family of Persian sentence embedding models designed to enhance semantic understanding for Persian. The models are released in two sizes{---}Small (123M parameters) and Large (353M parameters){---}both built upon the TookaBERT backbone. Tooka-SBERT is pretrained on the Targoman News corpus and fine-tuned using high-quality synthetic Persian sentence pair datasets to improve semantic alignment. We evaluate Tooka-SBERT on PTEB, a Persian adaptation of the MTEB benchmark, where the Large model achieves an average score of 70.54{\%} and the Small model 69.49{\%}, outperforming some strong multilingual baselines. Tooka-SBERT provides a compact and high-performing open-source solution for Persian sentence representation, with efficient inference suitable for both GPU and CPU environments. Our models are publicly available on Hugging Face, and the corresponding benchmark results can be viewed on the PTEB Leaderboard."
}