Update README.md

README.md

@@ -1,31 +1,21 @@
----
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: input_ids
-    sequence: int32
-  - name: length
-    dtype: int64
-  - name: source_domain
-    dtype: string
-  - name: writing_style_average
-    dtype: float64
-  - name: required_expertise_average
-    dtype: float64
-  - name: facts_and_trivia_average
-    dtype: float64
-  - name: educational_value_average
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 2010865867591
-    num_examples: 254141282
-  download_size: 1055335695720
-  dataset_size: 2010865867591
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+## 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}
+}
+```