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