---
task_categories:
- text-classification
language:
- en
tags:
- data quality rating
size_categories:
- 1M<n<10M
---

# PRRC Rater Training and Evaluation Dataset

## Dataset Description

This dataset contains the full training and evaluation data for the PRRC rater models described in [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://arxiv.org/abs/2504.14194). It is designed for training and benchmarking models that score text along four key quality dimensions: **Professionalism, Readability, Reasoning, and Cleanliness**.

- **Source**: Subset of SlimPajama-627B, annotated for PRRC dimensions
- **Purpose**: Supervised training and evaluation of PRRC raters (ModernBERT models)
- **Annotation**: Each sample is labeled by Llama-3.3-70B-Instruct and/or human annotators, then used to fine-tune and benchmark PRRC raters

## Dataset Statistics

- **Total samples**: ~1M (split into train/dev/test)
- **Quality metrics**: 4 PRRC dimensions (Professionalism, Readability, Reasoning, Cleanliness)
- **Domains**: Diverse (CommonCrawl, C4, GitHub, Books, ArXiv, Wikipedia, StackExchange)
- **Annotation coverage**: 100% of included samples

## PRRC Quality Dimensions

- **Professionalism**: Degree of expertise and prerequisite knowledge required
- **Readability**: Clarity, coherence, and ease of understanding
- **Reasoning**: Complexity of logical reasoning and analytical thinking
- **Cleanliness**: Formatting, completeness, and absence of noise/irrelevant content

Each dimension is rated on a 0–5 scale, with detailed prompt criteria provided in the [prompts/](./prompts/) directory of the GitHub repo.

## Dataset Structure

Each example in the dataset has the following structure:

```python
{
    "id": "unique_document_id",
    "content": "Main text content of the document",
    "source": "domain_name",  # e.g., "arxiv", "github", "wikipedia", etc.
    "professionalism": int,   # 0-5
    "readability": int,       # 0-5
    "reasoning": int,         # 0-5
    "cleanliness": int        # 0-5
}
```

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the full PRRC rater dataset
dataset = load_dataset("opendatalab/Meta-rater-PRRC-Rater-dataset")

# Access splits
train = dataset["train"]
dev = dataset["validation"]
test = dataset["test"]
```


## Applications

- **Supervised training** of PRRC rater models (e.g., ModernBERT)
- **Benchmarking** and evaluation of text quality raters
- **Prompt engineering** and ablation studies for quality annotation
- **Data-centric LLM research**: Understanding the impact of different quality dimensions

## Annotation Process

- **Initial annotation**: Llama-3.3-70B-Instruct (and/or human) rates each sample for all four PRRC dimensions using detailed prompts
- **Quality control**: Manual review and cleaning
- **Splitting**: Data is split into train/dev/test for robust evaluation

## 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. Please refer to the original SlimPajama repository for licensing 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**