--- license: odc-by language: - en task_categories: - text-classification tags: - common-crawl - web - human-written - pre-chatgpt - llm-detection - ai-text-detection - machine-generated-text-detection pretty_name: cc-re-2021 size_categories: - 100K= 0.925`, and `tokens_per_char` in [0.13, 0.3]. Formats in {'Spam / Ads': 0.01} are deterministically downsampled to the given keep-fraction (the `Spam / Ads` label was measured ~96% machine-generated junk in a manual audit; a 1% remnant is retained deliberately). Cross-year hygiene: rows whose exact text appears in the other published cc-re year are excluded per `cross_year_exclude` ({2021: 2020}), so the two years share no identical documents and can serve as leakage-free train/val splits. Seeded and reproducible; see `reports/selection_report.json` for the full funnel, filter exclusion counts, and per-cell counts. ## Test-set decontamination On 2026-09-19, 248 of the 500,000 selected rows were removed because they overlap the held-out human benchmark [`rounaksaha12/ai-det-test-human-refined`](https://huggingface.co/datasets/rounaksaha12/ai-det-test-human-refined) (1,689 FineWeb documents, 2013–2020 crawls), leaving **499,752 rows**. Every row was compared with both the benchmark's `text_refined` and `text_original` on distinct word 8-grams of the lowercased, NFKC-normalised `\w+` token stream, and removed if any of: - its URL equals a benchmark URL (scheme, `www.`, fragment and trailing slash ignored); - it shares **≥ 25 distinct 8-grams** with a single benchmark document (a verbatim passage of ~32+ words); - **≥ 5%** of its 8-grams occur in a single benchmark document, or ≥ 5% of a benchmark document's 8-grams occur in it. This is deliberately much stricter than near-duplicate detection: besides re-crawled copies of benchmark pages it removes pages that only share a template, a long quoted passage (scripture, statutes, licences) or a site-wide footer with a benchmark document. After removal, no remaining row shares more than 24 8-grams or 5% containment with any benchmark document. Removed rows, the rule that fired and the matched benchmark `doc_id` are listed in `reports/test_decontamination_removed.csv`. The Pipeline description, the statistics below and the other files under `reports/` describe the corpus as selected, before this removal (a 0.050% change). ## Schema | column | type | description | |---|---|---| | `boiler_lines_removed` | int32 | Lines removed by the deterministic page-furniture scrubber (0 = untouched) | | `crawl` | string | CommonCrawl dump id, e.g. `CC-MAIN-2020-24` | | `date` | string | Page fetch time (WARC-Date, ISO 8601) | | `dclm_score` | float32 | DCLM fastText (`fasttext-oh-eli5`) `__label__hq` probability | | `eflaw_readability` | — | | | `format` | string | WebOrganizer format label (24-way) | | `format_prob` | float32 | Format classifier softmax confidence | | `id` | string | Stable unique id: `/` | | `language_score` | float32 | fastText lid.176 P(en); kept only if ≥ 0.65 | | `n_emails` | int32 | Count of email addresses in `text` (FineWeb PII regex); text is NOT masked | | `n_ips` | int32 | Count of public IPv4 addresses in `text` (FineWeb PII regex); text is NOT masked | | `text` | string | Extracted clean text (trafilatura; never LLM-generated or rewritten) | | `tokens_per_char` | float32 | GneissWeb extreme-token stat: tokens / chars (GPT-2) | | `topic` | string | WebOrganizer topic label (24-way) | | `topic_prob` | float32 | Topic classifier softmax confidence | | `ufw_score` | float32 | Ultra-FineWeb-en fastText quality score (P(pos)) | | `url` | string | Source page URL (WARC-Target-URI) | | `word_count` | int32 | Whitespace word count | ## Statistics ### Overview | Statistic | Value | |---|---| | Rows | 499,752 | | Parquet shards | 1 | | Total words | 389,643,384 | | Total text characters | 2,278,657,811 | | Crawls | 9 | | Fetch-date range | 2021-01-15T13:41:43Z → 2021-12-09T09:04:59Z | ### Distributions | Metric | Mean | Median | p5 | p95 | Min | Max | |---|---|---|---|---|---|---| | Words per document | 779.3 | 444 | 97 | 2,350 | 50 | 97,179 | | Characters per document | 4,557 | 2,605 | 562 | 1.371e+04 | 224 | 543,799 | | Ultra-FineWeb quality score | 0.151 | 0.02891 | 0.0003666 | 0.8197 | 0 | 1 | ### Rows per crawl | Crawl | Rows | Share | |---|---|---| | CC-MAIN-2021-04 | 53,799 | 10.76% | | CC-MAIN-2021-10 | 54,494 | 10.90% | | CC-MAIN-2021-17 | 64,380 | 12.88% | | CC-MAIN-2021-21 | 52,561 | 10.51% | | CC-MAIN-2021-25 | 48,741 | 9.75% | | CC-MAIN-2021-31 | 61,034 | 12.21% | | CC-MAIN-2021-39 | 53,974 | 10.79% | | CC-MAIN-2021-43 | 62,652 | 12.53% | | CC-MAIN-2021-49 | 48,365 | 9.67% | ### Topics | Topic | Rows | Share | |---|---|---| | Sports & Fitness | 21,788 | 4.36% | | Travel | 21,777 | 4.36% | | Health | 21,773 | 4.35% | | Entertainment | 21,771 | 4.35% | | Crime & Law | 21,761 | 4.35% | | Food & Dining | 21,746 | 4.35% | | Industrial | 21,745 | 4.35% | | Social Life | 21,744 | 4.35% | | Games | 21,744 | 4.35% | | Religion | 21,740 | 4.35% | | Fashion & Beauty | 21,739 | 4.35% | | History | 21,737 | 4.35% | | Home & Hobbies | 21,736 | 4.35% | | Hardware | 21,736 | 4.35% | | Science & Tech. | 21,733 | 4.35% | | Education & Jobs | 21,732 | 4.35% | | Literature | 21,726 | 4.35% | | Finance & Business | 21,725 | 4.34% | | Art & Design | 21,724 | 4.34% | | Transportation | 21,720 | 4.34% | | Politics | 21,719 | 4.34% | | Software Dev. | 21,698 | 4.34% | | Software | 21,686 | 4.34% | ### Formats | Format | Rows | Share | |---|---|---| | Listicle | 28,832 | 5.77% | | Personal Blog | 28,814 | 5.76% | | Audio Transcript | 28,811 | 5.76% | | Tutorial | 28,801 | 5.76% | | News Article | 28,795 | 5.76% | | Nonfiction Writing | 28,794 | 5.76% | | About (Org.) | 28,789 | 5.76% | | News (Org.) | 28,777 | 5.76% | | Academic Writing | 28,769 | 5.75% | | Product Page | 28,768 | 5.75% | | Creative Writing | 28,767 | 5.75% | | Knowledge Article | 28,756 | 5.75% | | User Review | 28,738 | 5.75% | | Comment Section | 28,689 | 5.74% | | About (Pers.) | 26,970 | 5.39% | | Customer Support | 21,590 | 4.32% | | Q&A Forum | 20,971 | 4.19% | | FAQ | 15,652 | 3.13% | | Legal Notices | 8,876 | 1.78% | | Documentation | 2,537 | 0.51% | | Spam / Ads | 504 | 0.10% | ### Topic × format cells Non-empty cells: **349** (of 24 × 24 = 576 possible). Largest cell: Literature × Creative Writing (15,772 rows); smallest non-empty cell: Software Dev. × Spam / Ads (1 rows). Top 30 cells: | Topic | Format | Rows | |---|---|---| | Literature | Creative Writing | 15,772 | | Food & Dining | Tutorial | 9,815 | | Industrial | Product Page | 9,683 | | Entertainment | User Review | 9,258 | | Art & Design | Product Page | 8,943 | | Travel | Personal Blog | 8,889 | | Politics | News (Org.) | 8,465 | | Science & Tech. | Nonfiction Writing | 8,221 | | Home & Hobbies | Listicle | 8,141 | | Crime & Law | Knowledge Article | 7,743 | | Science & Tech. | Academic Writing | 6,977 | | Education & Jobs | About (Org.) | 6,641 | | Games | Comment Section | 6,371 | | Fashion & Beauty | Personal Blog | 6,162 | | Sports & Fitness | News Article | 5,850 | | History | Knowledge Article | 5,749 | | Politics | Audio Transcript | 5,624 | | Software Dev. | Personal Blog | 5,543 | | Hardware | Product Page | 5,497 | | Travel | Listicle | 5,426 | | Social Life | Nonfiction Writing | 5,415 | | History | Nonfiction Writing | 5,365 | | Hardware | News Article | 5,235 | | Transportation | Comment Section | 5,074 | | Software | Tutorial | 5,045 | | Religion | Knowledge Article | 4,861 | | Games | Personal Blog | 4,827 | | Hardware | Comment Section | 4,506 | | Education & Jobs | About (Pers.) | 4,337 | | Finance & Business | Customer Support | 4,215 | ## Known limitations (measured, from manual audits) Rates from hand-read random samples (n=100–120) and full-corpus scans; details in `reports/audit_post_remediation.md`: | Limitation | Measured rate | Note | |---|---|---| | Pre-LLM machine text (spinners, raw MT, doorway/template pages) | ~5% of rows (CI 0.7–9.3) | not concentrated in any filterable label | | Content-farm / SEO filler (human-typed, formulaic) | ~11% | possible home of undetectable pre-2022 neural text | | Page-furniture residue (bylines, widgets, notices) | ~1 in 4 rows has some | scrubber is precision-first, not exhaustive | | Extraction damage (pipe residue, dropped lists, merged words) | 2.8% / ~9% / ~10% | inherent to HTML→text; not remediable without re-extraction | | Scraped/republished third-party content | ~12% | `url`/`date` are not reliable provenance for these | | Scrubber over-removal (real lines lost) | ~0.1% of rows | rows with `boiler_lines_removed >= 2` are the risk set | | Low-confidence format labels | `format_prob < 0.5` on ~10% | filter on `format_prob` before conditioning on `format` | Also note: `word_count` and all scores (`language_score`, `ufw_score`, `dclm_score`, `tokens_per_char`, `eflaw_readability`) were computed on the **pre-scrub** text; rows with `boiler_lines_removed > 0` (~5.5%) are slightly shorter than `word_count` claims. ## Provenance, limitations, and bias - **Source**: [CommonCrawl](https://commoncrawl.org) 2021 crawls, fetched from `https://data.commoncrawl.org`. Use of the underlying data is subject to the [CommonCrawl Terms of Use](https://commoncrawl.org/terms-of-use). - **Extraction bias**: text is trafilatura-extracted with `favour_precision=True`; most boilerplate, navigation, and comments are removed (extraction + C4 line rules + the deterministic scrubber), but a minority of rows retain some page-furniture residue, and extraction can damage formatting (lost lists, table→pipe residue). Detectors trained on this corpus should compare against AI text formatted the same way (plain extracted text, no HTML). - **Selection bias**: English only (lid.176 ≥ 0.65), quality floors as above, and topic × format rebalancing — the corpus is *not* a natural sample of the 2021 web. Cells that are near-empty on the real web (e.g. Adult × Academic Writing) remain small; see the cell table above and `reports/selection_report.json`. - **Residual contamination**: pre-ChatGPT does not mean zero machine text — 2021 web pages can contain template-generated, machine-translated, or spun/SEO text. The quality floors and junk fences remove most of it, but no guarantee is absolute. - **Content warning**: despite URL blocklists and quality filtering, web text may contain offensive, inaccurate, or otherwise objectionable content, and may mention personal data present on public 2021 web pages. - **PII is deliberately NOT masked.** This corpus exists to train detectors that distinguish human from AI text; masking emails/IPs with stock replacement strings (as FineWeb does) would inject an "anonymization artifact" that appears only in the human class — a spurious, learnable shortcut — and would make the text something no human actually wrote. The text is redistributed exactly as it appeared on the public 2021 web (as in CommonCrawl itself). Per-row `n_emails` and `n_ips` columns count FineWeb-definition PII hits (email regex; public IPv4) so downstream users can filter or apply their own masking if their use case requires it. - **Takedown**: to report content (including personal data) for removal, open a discussion on this repo. ## License The curation code and this compilation are released under [ODC-By 1.0](https://opendatacommons.org/licenses/by/1-0/) (like FineWeb); use of the underlying content remains subject to the [CommonCrawl Terms of Use](https://commoncrawl.org/terms-of-use). ## Citations This dataset composes methods from the following works — please cite them when using it: ```bibtex @inproceedings{penedo2024fineweb, title = {The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale}, author = {Penedo, Guilherme and Kydl{\'i}{\v{c}}ek, Hynek and Ben allal, Loubna and Lozhkov, Anton and Mitchell, Margaret and Raffel, Colin and Von Werra, Leandro and Wolf, Thomas}, booktitle = {NeurIPS Datasets and Benchmarks}, year = {2024}, url = {https://arxiv.org/abs/2406.17557} } @article{wang2025ultrafineweb, title = {Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data}, author = {Wang, Yudong and Sun, Zixuan and others}, journal = {arXiv preprint arXiv:2505.05427}, year = {2025}, url = {https://arxiv.org/abs/2505.05427} } @article{gneissweb2025, title = {GneissWeb: Preparing High Quality Data for LLMs at Scale}, author = {Gohari, Hajar Emami and Kadhe, Swanand Ravindra and others}, journal = {arXiv preprint arXiv:2502.14907}, year = {2025}, url = {https://arxiv.org/abs/2502.14907} } @inproceedings{wettig2025weborganizer, title = {Organize the Web: Constructing Domains Enhances Pre-Training Data Curation}, author = {Wettig, Alexander and Lo, Kyle and Min, Sewon and Hajishirzi, Hannaneh and Chen, Danqi and Soldaini, Luca}, booktitle = {ICML}, year = {2025}, url = {https://arxiv.org/abs/2502.10341} } ``` Built with [datatrove](https://github.com/huggingface/datatrove). Data from [CommonCrawl](https://commoncrawl.org). Published 2026-08-31.