Update README.md

README.md

@@ -3,6 +3,8 @@ pretty_name: QuRatedPajama-1B_tokens_for_analysis
 ---
 ## QuRatedPajama
 
+**Paper:** [QuRating: Selecting High-Quality Data for Training Language Models](https://arxiv.org/pdf/2402.09739.pdf)
+
 This dataset is a 1B token subset derived from [princeton-nlp/QuRatedPajama-260B](https://huggingface.co/datasets/princeton-nlp/QuRatedPajama-260B), which is a 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) 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
@@ -13,16 +15,12 @@ This subset is useful for analysis of quality ratings. It unsupervised domain cl
 
 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**
 
-**_Guidance on Responsible Use_**
-
-In the paper, we document various types of bias that are present in the quality ratings/QuRater model (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper).
+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:
+**Citation:**
 ```
 @article{wettig2024qurating,
    title={QuRating: Selecting High-Quality Data for Training Language Models},