| language_creators: | |
| - found | |
| language: | |
| - en | |
| license: odc-by | |
| source_datasets: | |
| - c4 | |
| task_categories: | |
| - text-generation | |
| - fill-mask | |
| task_ids: | |
| - language-modeling | |
| - masked-language-modeling | |
| dataset_info: | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: score | |
| dtype: float64 | |
| splits: | |
| - name: train | |
| num_bytes: 406518157.4620394 | |
| num_examples: 302379 | |
| download_size: 245358543 | |
| dataset_size: 406518157.4620394 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| The estimated top 10% of highest n-token (mean 3,4,5) overlaps for each of the | |
| selected benchmark datasets (arc, truthful_qa, hellaswag, mmlu, humaneval) based | |
| on 1k samples, within the first 3M samples of C4. The top scoring sample | |
| datasets for each benchmark are then filtered again for top 30% scores and | |
| combined and exact-match de-duplicated. ~~Then the top 3% scores are removed | |
| because they likely have exact large n-token matches by chance such as exact | |
| dates or times that aren't actually relevant to the data.~~ (todo) | |
| This is meant to fascilitate a high-quality short continuation of pretraining | |
| for language models. |