| ## 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) | |
| **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} | |
| } | |
| ``` |