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README.md
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<img src="https://huggingface.co/datasets/AdaMLLab/TurMix/resolve/main/finetasks_turkish_main_results.png" width="900" alt="Finetasks benchmark scores, showing TurMix-Matched as SOTA.">
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TurMix ([https://arxiv.org/abs/2512.18834](https://arxiv.org/abs/2512.18834)) is a Turkish pretraining corpus containing 168 billion tokens across 219 million documents (in the minhash subset). Rather than scraping the web again, TurMix combines five publicly available Turkish datasets, applies Turkish-specific quality filtering, and performs cross-dataset deduplication.
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We train a 1.4B parameter language model through nanotron on 30 billion tokens to show that the `matched` subset of TurMix outperforms the previous state-of-the-art, [FineWeb-2 Turkish](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) (see [Appendix A9 in the Fineweb-2 paper](https://arxiv.org/pdf/2506.20920)), achieving a 5.5% relative improvement. Furthermore, the `minhash_deduped` subset performs competitively with over 2× the total number of tokens.
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<img src="https://huggingface.co/datasets/AdaMLLab/TurMix/resolve/main/finetasks_turkish_main_results.png" width="900" alt="Finetasks benchmark scores, showing TurMix-Matched as SOTA.">
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<p align="center">
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<a href="https://huggingface.co/collections/AdaMLLab/mixminmatch">
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<img src="https://img.shields.io/badge/🤗_Collection-MixMinMatch-blue" alt="MixMinMatch Collection">
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</a>
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</p>
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TurMix ([https://arxiv.org/abs/2512.18834](https://arxiv.org/abs/2512.18834)) is a Turkish pretraining corpus containing 168 billion tokens across 219 million documents (in the minhash subset). Rather than scraping the web again, TurMix combines five publicly available Turkish datasets, applies Turkish-specific quality filtering, and performs cross-dataset deduplication.
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We train a 1.4B parameter language model through nanotron on 30 billion tokens to show that the `matched` subset of TurMix outperforms the previous state-of-the-art, [FineWeb-2 Turkish](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) (see [Appendix A9 in the Fineweb-2 paper](https://arxiv.org/pdf/2506.20920)), achieving a 5.5% relative improvement. Furthermore, the `minhash_deduped` subset performs competitively with over 2× the total number of tokens.
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