arXiv:2510.21364v2 Announce Type: replace Abstract: Transformer models have revolutionized NLP, yet many morphologically rich languages remain underrepresented in large-scale pre-training efforts. With SindBERT, we set out to chart the seas of Turkish NLP, providing the first large-scale RoBERTa-based encoder for Turkish. Trained from scratch on 312~GB of Turkish text (mC4, OSCAR23, Wikipedia), SindBERT is released in both base and large configurations, representing the first large-scale encoder-only language model available for Turkish. We evaluate SindBERT on part-of-speech tagging, named en
The proliferation of advanced transformer models has highlighted the linguistic gap for morphologically rich languages like Turkish, prompting focused efforts to build foundational NLP models for these specific contexts.
This development marks a significant step towards linguistic inclusivity in AI, enabling advanced NLP applications for Turkish and setting a precedent for other underrepresented languages, reducing reliance on models trained predominantly on English data.
Turkish NLP capabilities are substantially enhanced with a large-scale, RoBERTa-based encoder, enabling more accurate and nuanced AI applications in a language previously underserved by such foundational models.
- · Turkish AI developers
- · Turkish tech companies
- · Turkish-speaking internet users
- · Linguistic diversity advocates
- · Companies relying on poor quality Turkish NLP
- · Developers solely focused on English NLP models
Improved NLP performance for Turkish language tasks across various applications.
Increased innovation and development of AI-powered services tailored for the Turkish market and culture.
Potential for other nations with underrepresented languages to initiate similar large-scale domestic AI model development.
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