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Alptekinege
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TurMix

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Text Generation
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Turkish
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100M - 1B
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Alptekinege SultanR commited on
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Duplicate from AdaMLLab/TurMix

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Co-authored-by: Sultan Alrashed <SultanR@users.noreply.huggingface.co>

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README.md ADDED
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+ ---
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+ language:
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+ - tr
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+ license: other
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+ task_categories:
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+ - text-generation
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+ arxiv: 2512.18834
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+ configs:
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+ - config_name: minhash_deduped
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+ data_files:
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+ - split: train
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+ path: minhash_deduped/**/*.parquet
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+ - config_name: quality_filtered
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+ data_files:
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+ - split: train
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+ path: quality_filtered/**/*.parquet
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+ - config_name: matched
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+ data_files:
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+ - split: train
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+ path: consensus/*.parquet
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+ default: minhash_deduped
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+ ---
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+
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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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+
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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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+
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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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+
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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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+
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+ ## Subsets
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+
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+ | Subset | Documents | Tokens | Description |
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+ |--------|-----------|--------|-------------|
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+ | `quality_filtered` | 394.0M | 307.2B | Quality-filtered data before deduplication |
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+ | `minhash_deduped` | 219.1M | 167.6B | Document-level MinHash deduplication |
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+ | `matched` | 67.6M | 56.0B | Documents appearing in 2+ source datasets |
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+
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+ The matched subset uses cross-dataset agreement as a signal for quality.
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("AdaMLLab/TurMix", "minhash_deduped")
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+ ds = load_dataset("AdaMLLab/TurMix", "quality_filtered")
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+ ds = load_dataset("AdaMLLab/TurMix", "matched")
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+ ```
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+
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+ ## Sources
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+
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+ Tokens were counted using `meta-llama/Llama-3.2-3B`'s tokenizer.
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+
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+ | Source | Tokens (MinHash) | Documents (MinHash) |
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+ |--------|------------------|---------------------|
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+ | HPLT 2.0 | 46.0B | 53.7M |
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+ | FineWeb-2 | 41.9B | 54.5M |
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+ | CulturaX | 35.8B | 47.9M |
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+ | C4 | 25.3B | 36.5M |
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+ | VNGRS-Web | 18.7B | 26.5M |
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+ | **Total** | **167.6B** | **219.1M** |
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+
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+ ## Pipeline
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+
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+ 1. Quality filtering with Turkish-specific thresholds (terminal punctuation, repetition patterns, Latin script ratio, language identification)
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+ 2. Document-level MinHash deduplication (5-gram shingles, 14 bands, 8 hashes per band, similarity threshold 0.8)
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+ 3. Cross-source matching to identify documents appearing in 2+ independent sources
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+
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+ ## Citation
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+
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+ ```bib
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+ @misc{alrashed2025mixminmatch,
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+ title={Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets},
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+ author={Sultan Alrashed and Francesco Orabona},
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+ year={2025},
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+ eprint={2512.18834v2},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2512.18834v2},
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+ }
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+ ```
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+
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+ ## License
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+
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+ See individual source dataset licenses.
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