---
task_categories:
- text-generation
language:
- en
tags:
- pretrain
size_categories:
- 10B<n<100B
---

# Top 30B token SlimPajama Subset selected by the Cleanliness rater

This repository contains the dataset described in the paper [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://huggingface.co/papers/2504.14194).

Code: https://github.com/opendatalab/Meta-rater

## Dataset Description

This dataset contains the top 30B tokens from the SlimPajama-627B corpus, selected using the **Cleanliness** dimension of the PRRC (Professionalism, Readability, Reasoning, Cleanliness) framework. Each document in this subset is scored and filtered by a ModernBERT-based rater fine-tuned to assess the formatting, completeness, and absence of noise or irrelevant content in the text.

- **Source**: SlimPajama-627B Annotated Dataset
- **Selection**: Top 30B tokens by PRRC-Cleanliness score
- **Quality metric**: Cleanliness (0–5 scale, see below)
- **Annotation coverage**: 100% of selected subset

## Dataset Statistics

- **Total tokens**: 30B (subset of SlimPajama-627B)
- **Selection method**: Top-ranked by PRRC-Cleanliness ModernBERT rater
- **Domains**: Same as SlimPajama (CommonCrawl, C4, GitHub, Books, ArXiv, Wikipedia, StackExchange)
- **Annotation**: Each document has a cleanliness score (0–5)

## Cleanliness Quality Metric

**Cleanliness** evaluates the formatting, completeness, and absence of noise or irrelevant content in the text. Higher scores indicate well-formatted, complete, and clean data, while lower scores reflect noisy, incomplete, or poorly formatted content.

- **0–1**: Serious or obvious issues affecting fluency or completeness
- **2–3**: Some problems, but not seriously affecting reading
- **4–5**: Minor or no problems; text is clean and well-formatted

Scores are assigned by a ModernBERT model fine-tuned on Llama-3.3-70B-Instruct annotations, as described in the Meta-rater paper.

## Annotation Process

- **Initial annotation**: Llama-3.3-70B-Instruct rated 500k+ SlimPajama samples for cleanliness
- **Model training**: ModernBERT fine-tuned on these annotations
- **Scoring**: All SlimPajama documents scored by ModernBERT; top 30B tokens selected

## Citation

If you use this dataset, please cite:

```bibtex
@article{zhuang2025meta,
  title={Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models},
  author={Zhuang, Xinlin and Peng, Jiahui and Ma, Ren and Wang, Yinfan and Bai, Tianyi and Wei, Xingjian and Qiu, Jiantao and Zhang, Chi and Qian, Ying and He, Conghui},
  journal={arXiv preprint arXiv:2504.14194},
  year={2025}
}
```

## License

This dataset is released under the same license as the original SlimPajama dataset. See the original SlimPajama repository for details.

## Contact

- **Project Lead**: Ren Ma (maren@pjlab.org.cn)
- **Corresponding Author**: Conghui He (heconghui@pjlab.org.cn)
- **Issues**: [GitHub Issues](https://github.com/opendatalab/Meta-rater/issues)

---

**Made with ❤️ by the OpenDataLab team**