README.md

metadata

license: cc-by-4.0
language:
  - hi
size_categories:
  - 10M<n<100M
task_categories:
  - text-generation
tags:
  - pretraining
  - hindi
  - deduplication
  - quality-filtered
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: consensus
    data_files:
      - split: train
        path: consensus/*.parquet

HinMix: Hindi Pretraining Data Mix

A high-quality Hindi pretraining dataset created by combining, filtering, and deduplicating multiple sources.

Dataset Description

This dataset contains Hindi text from multiple web crawl sources, processed through a quality filtering and MinHash deduplication pipeline.

Sources

• C4 (mC4 Hindi subset)
• CulturaX (Hindi)
• Fineweb-2 (hin_Deva)
• HPLT-2 (hin_Deva)
• Sangraha (verified and unverified Hindi splits)

Subsets

1. minhash_deduped (Recommended)

MinHash-deduplicated data. Each source was deduplicated individually to remove near-duplicate documents.

from datasets import load_dataset
ds = load_dataset("AdaMLLab/HinMix", "minhash_deduped")

Statistics:

• ~60M documents
• 136GB compressed

2. quality_filtered

Quality-filtered data before deduplication. Use this if you want to apply your own deduplication.

from datasets import load_dataset
ds = load_dataset("AdaMLLab/HinMix", "quality_filtered")

Statistics:

• ~99M documents
• 231GB compressed

3. consensus

Documents that appear in 2+ sources (exact text match). These are high-confidence documents verified across multiple crawls.

from datasets import load_dataset
ds = load_dataset("AdaMLLab/HinMix", "consensus")

Statistics:

• 1.92M documents
• 3.7GB compressed

Schema:

• text: Document text
• id: Primary document ID
• sources: List of sources where document appears (e.g., ["c4", "culturax"])
• all_ids: All document IDs from all sources
• metadata: Additional metadata

Quality Filtering

Documents were filtered based on:

• Language identification (Hindi/Devanagari script ratio)
• Document length constraints
• Line quality metrics
• Repetition detection
• Boilerplate/policy phrase removal

Filter thresholds based on Fineweb-2 Hindi configuration.

Citation

If you use this dataset, please cite:

@dataset{hinmix2024,
  title={HinMix: Hindi Pretraining Data Mix},
  author={AdaMLLab},
  year={2024},
  publisher={Hugging Face}
}

License

This dataset is released under CC-BY-4.0. Individual source datasets may have their own licenses.