README.md

metadata

language:
  - hi
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

HinMix (https://arxiv.org/abs/2512.18834) is a Hindi pretraining corpus containing 76 billion tokens across 60 million documents (in the minhash subset). Rather than scraping the web again, HinMix combines six publicly available Hindi datasets, applies Hindi-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 HinMix outperforms the previous state-of-the-art, CulturaX Hindi (see Appendix A9 in the Fineweb-2 paper). The minhash_deduped subset achieves an 11.6% relative improvement, while the matched subset achieves an 8.1% relative improvement.

Subsets

Subset	Documents	Tokens	Description
quality_filtered	99.6M	130.3B	Quality-filtered data before deduplication
minhash_deduped	59.6M	76.2B	Document-level MinHash deduplication
matched	19.8M	27.1B	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/HinMix", "minhash_deduped")
ds = load_dataset("AdaMLLab/HinMix", "quality_filtered")
ds = load_dataset("AdaMLLab/HinMix", "matched")

Sources

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

Source	Tokens (MinHash)	Documents (MinHash)
FineWeb-2	20.0B	17.1M
CulturaX	16.6B	11.5M
Sangraha (unverified)	11.5B	8.9M
HPLT 2.0	10.2B	6.7M
Sangraha (verified)	10.1B	9.1M
C4	7.7B	6.3M
Total	76.2B	59.6M

Pipeline

1. Quality filtering with Hindi-specific thresholds (Devanagari script ratio, repetition patterns, 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.