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Datasets:
AdaMLLab
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JpnMix

Tasks:
Text Generation
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Languages:
Japanese
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  1. README.md +82 -0
  2. quality_filtered/00012.parquet +3 -0
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README.md ADDED
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+ ---
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+ language:
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+ - ja
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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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+ <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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+ JpnMix ([https://arxiv.org/abs/2512.18834](https://arxiv.org/abs/2512.18834)) is a Japanese pretraining corpus built by combining five publicly available Japanese datasets, applying Japanese-specific quality filtering, and performing cross-dataset deduplication.
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+
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+ ## Subsets
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+
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+ | Subset | Description |
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+ |--------|-------------|
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+ | `quality_filtered` | Quality-filtered data before deduplication |
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+ | `minhash_deduped` | Document-level MinHash deduplication |
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+ | `matched` | 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/JpnMix", "minhash_deduped")
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+ ds = load_dataset("AdaMLLab/JpnMix", "quality_filtered")
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+ ds = load_dataset("AdaMLLab/JpnMix", "matched")
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+ ```
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+
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+ ## Sources
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+
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+ - FineWeb-2 (`HuggingFaceFW/fineweb-2`, `jpn_Jpan`)
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+ - HPLT 2.0 (`HPLT/HPLT2.0_cleaned`, `jpn_Jpan`)
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+ - CulturaX (`uonlp/CulturaX`, `ja`)
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+ - C4 (`allenai/c4`, `ja`)
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+ - FinePDFs (`HuggingFaceFW/finepdfs`, `jpn_Jpan`)
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+
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+ ## Pipeline
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+
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+ 1. Quality filtering with Japanese-specific thresholds (CJK+Kana script ratio, no-space-language adjustments for word-level metrics, repetition patterns)
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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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