File size: 3,427 Bytes
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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
---
<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.">
<p align="center">
<a href="https://huggingface.co/collections/AdaMLLab/mixminmatch">
<img src="https://img.shields.io/badge/🤗_Collection-MixMinMatch-blue" alt="MixMinMatch Collection">
</a>
</p>
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.
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.
## 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
```python
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
```bib
@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. |