README.md

metadata

language:
  - tr
license: other
task_categories:
  - text-generation
arxiv: 2512.18834
configs:
  - config_name: minhash_deduped
    data_files:
      - split: train
        path: minhash_deduped/**/*.parquet
  - config_name: quality_filtered
    data_files:
      - split: train
        path: quality_filtered/**/*.parquet
  - config_name: matched
    data_files:
      - split: train
        path: consensus/*.parquet
default: minhash_deduped

TurMix (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.

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 (see Appendix A9 in the Fineweb-2 paper), achieving a 5.5% relative improvement. Furthermore, the minhash_deduped subset performs competitively with over 2× the total number of tokens.

Subsets

Subset	Documents	Tokens	Description
quality_filtered	394.0M	307.2B	Quality-filtered data before deduplication
minhash_deduped	219.1M	167.6B	Document-level MinHash deduplication
matched	67.6M	56.0B	Documents appearing in 2+ source datasets

The matched subset uses cross-dataset agreement as a signal for quality.

Usage

from datasets import load_dataset

ds = load_dataset("AdaMLLab/TurMix", "minhash_deduped")
ds = load_dataset("AdaMLLab/TurMix", "quality_filtered")
ds = load_dataset("AdaMLLab/TurMix", "matched")

Sources

Tokens were counted using meta-llama/Llama-3.2-3B's tokenizer.

Source	Tokens (MinHash)	Documents (MinHash)
HPLT 2.0	46.0B	53.7M
FineWeb-2	41.9B	54.5M
CulturaX	35.8B	47.9M
C4	25.3B	36.5M
VNGRS-Web	18.7B	26.5M
Total	167.6B	219.1M

Pipeline

1. Quality filtering with Turkish-specific thresholds (terminal punctuation, repetition patterns, Latin script ratio, language identification)
2. Document-level MinHash deduplication (5-gram shingles, 14 bands, 8 hashes per band, similarity threshold 0.8)
3. Cross-source matching to identify documents appearing in 2+ independent sources

Citation

@misc{alrashed2025mixminmatch,
      title={Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets}, 
      author={Sultan Alrashed and Francesco Orabona},
      year={2025},
      eprint={2512.18834v2},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.18834v2}, 
}

License

See individual source dataset licenses.