KyrgyzBERT: A Compact, Efficient Language Model for Kyrgyz NLP

• URL: http://arxiv.org/abs/2511.20182v1
• Date: Tue, 25 Nov 2025 11:05:53 GMT
• Title: KyrgyzBERT: A Compact, Efficient Language Model for Kyrgyz NLP
• Authors: Adilet Metinov, Gulida M. Kudakeeva, Gulnara D. Kabaeva,
• Abstract summary: We introduce KyrgyzBERT, the first publicly available monolingual BERT-based language model for Kyrgyz.<n>The model has 35.9M parameters and uses a custom tokenizer designed for the language's morphological structure.<n>Kyrgyz-sst2 is a sentiment analysis benchmark built by translating the Stanford Sentiment Treebank and manually annotating the full test set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Kyrgyz remains a low-resource language with limited foundational NLP tools. To address this gap, we introduce KyrgyzBERT, the first publicly available monolingual BERT-based language model for Kyrgyz. The model has 35.9M parameters and uses a custom tokenizer designed for the language's morphological structure. To evaluate performance, we create kyrgyz-sst2, a sentiment analysis benchmark built by translating the Stanford Sentiment Treebank and manually annotating the full test set. KyrgyzBERT fine-tuned on this dataset achieves an F1-score of 0.8280, competitive with a fine-tuned mBERT model five times larger. All models, data, and code are released to support future research in Kyrgyz NLP.

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