| --- |
| language: |
| - en |
| size_categories: |
| - 100B<n<1T |
| --- |
| # WebOrganizer/Corpus-200B |
| [[Paper](https://arxiv.org/abs/2502.10341)] [[Website](https://weborganizer.allenai.org)] [[GitHub](https://github.com/CodeCreator/WebOrganizer)] |
|
|
| This dataset is a pre-processed version of the `1b-1x` CommonCrawl pool from DataComps-LM cleaned with |
| (1) [RefinedWeb filters](https://github.com/mlfoundations/dclm/blob/main/baselines/baselines_configs/dclm_baseline_refinedweb.yaml) and |
| (2) [BFF deduplication](https://github.com/mlfoundations/dclm/tree/main/dedup/bff). |
|
|
| We provide the resulting 200B token corpus annotated with two quality scores, WebOrganizer domains, and k-means scores. |
|
|
| __Download the dataset by cloning the repository with Git LFS instead of HuggingFace's `load_dataset()`.__ |
| |
| The dataset has the following folder structure: |
| ```bash |
| Corpus-200B/ |
| documents/ # Pre-processed web documents |
| - CC_shard_00000000_processed.jsonl.zst |
| - CC_shard_00000001_processed.jsonl.zst |
| - ... |
| tokens/ # number of tokens per document (GPT-NeoX tokenizer) |
| - CC_shard_00000000_processed.npy |
| - CC_shard_00000001_processed.npy |
| - ... |
| scores_dclm-fasttext/ # DCLM-fasttext score |
| - CC_shard_00000000_processed.npy |
| - ... |
| scores_fineweb-edu/ # FineWeb-Edu score |
| - CC_shard_00000000_processed.npy |
| - ... |
| scores_fineweb-edu__rounded/ # Rounded FineWeb-Edu score |
| - CC_shard_00000000_processed__rounded.npy |
| - ... |
| domains_topics/ # TopicClassifier annotations |
| - CC_shard_00000000_processed__choice.npy # index of top choice |
| - ... |
| domain_topics__logits/ |
| - CC_shard_00000000_processed__logits.npy # logits for each topic |
| - ... |
| domains_formats/ # FormatClassifier annotations |
| - CC_shard_00000000_processed__choice.npy # index of top choice |
| - ... |
| domains_formats/ # FormatClassifier annotations |
| - CC_shard_00000000_processed__logits.npy # logits for each format |
| - ... |
| domains_clusters-k24/ # K-means clusters |
| - CC_shard_00000000_processed.npy # cluster assignment for each document |
| - ... |
| ``` |
| We also include statistics about the presence and co-occurence of domains in the `domain_statistics/` folder, computed with the `domain_statistics.py` script. |
| |
| ## Citation |
|
|
| If you make use of this pre-processed corpus in your work, please cite: |
| ```bibtex |
| @article{wettig2025organize, |
| title={Organize the Web: Constructing Domains Enhances Pre-Training Data Curation}, |
| author={Alexander Wettig and Kyle Lo and Sewon Min and Hannaneh Hajishirzi and Danqi Chen and Luca Soldaini}, |
| journal={arXiv preprint arXiv:2502.10341}, |
| year={2025} |
| } |
| ``` |
