---
pretty_name: QuRatedPajama-260B
---
## QuRatedPajama

A 260B token subset of [cerebras/SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B), annotated by [princeton-nlp/QuRater-1.3B](https://huggingface.co/princeton-nlp/QuRater-1.3B/tree/main) with sequence-level quality ratings across 4 criteria:
- **Educational Value** - e.g. the text includes clear explanations, step-by-step reasoning, or questions and answers
- **Facts & Trivia** - how much factual and trivia knowledge the text contains, where specific facts and obscure trivia are preferred over more common knowledge
- **Writing Style** - how polished and good is the writing style in the text
- **Required Expertise**: - how much required expertise and prerequisite knowledge is necessary to understand the text

In a pre-processing step, we split documents in into chunks of exactly 1024 tokens. We provide tokenization with the Llama-2 tokenizer in the `input_ids` column.


**Paper:** [QuRating: Selecting High-Quality Data for Training Language Models](https://arxiv.org/pdf/2402.09739.pdf)

**Guidance on Responsible Use**

In the paper, we document various types of bias that are present in the quality ratings (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper).
Hence, be aware that data selection with QuRating could have unintended and harmful effects on the language model that is being trained. We strongly recommend a comprehensive evaluation of the language model for these and other types of bias, particularly before real-world deployment. We hope that releasing the data/models can facilitate future research aimed at uncovering and mitigating such biases.

**Citation:**
```
@article{wettig2024qurating,
   title={QuRating: Selecting High-Quality Data for Training Language Models},
   author={Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen},
   journal={arXiv preprint 2402.09739},
   year={2024}
}
```