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README_PRRC_dataset.md

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+# 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**