| --- |
| 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<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| # cc-re-2021: pre-ChatGPT human-written English web text |
|
|
| **499,752 documents** of clean English, overwhelmingly human-written web text, |
| extracted from the 9 CommonCrawl |
| crawls of **2021**, globally deduplicated within the year (and sharing no |
| identical document with the sister year), annotated with |
| [WebOrganizer](https://arxiv.org/abs/2502.10341) topic × format labels, and |
| selected so the per-format and per-topic row totals are each as uniform as the |
| eligible pool allows (water-filled marginal targets + exact LP; scarce labels |
| contribute their entire pool — see `reports/selection_report.json` |
| `achieved_marginals` for the realized totals). |
|
|
| This corpus is the **"human" class for training and evaluating LLM-text |
| detectors**. Two properties make it suitable: |
|
|
| 1. **Temporal purity.** Every page was crawled in 2021 — before the release of |
| ChatGPT (Nov 2022) and before large-scale LLM text existed on the public web — |
| which rules out *chatbot-era* AI contamination by crawl date. Pre-LLM machine |
| text (synonym-spinner spam, raw machine translation, template/doorway pages) |
| does exist on the 2021 web; after remediation a manual audit measures its |
| residual at **~5% of rows** (95% CI 0.7–9.3; see Known limitations). |
| 2. **No LLM in the loop.** No LLM ever generated, rewrote, paraphrased, or |
| summarized any text in this dataset. Models are used *read-only*: language ID, |
| fastText quality scoring, and topic/format classification. The `text` bytes |
| are what [trafilatura](https://trafilatura.readthedocs.io/) extracted from |
| the original 2021 HTML, post-processed only by (a) FineWeb's standard |
| heuristic filters (the C4 rules also drop boilerplate lines — cookie/policy |
| notices, javascript mentions, sub-3-word lines — and strip bracketed |
| citation markers, exactly as in FineWeb), and (b) a deterministic, |
| removal-only line scrubber for page furniture (cookie banners, share |
| widgets, bylines/postmeta, breadcrumbs, pagination, footer notices — 18 |
| documented rules, no model involved; `boiler_lines_removed` records the |
| per-row count, ~5.6% of rows touched, ~0.06% of characters). No masking or |
| any other rewriting is applied. Some navigation/UI residue still survives |
| in a minority of rows — deterministic scrubbing is precision-first, not |
| exhaustive. |
|
|
| Sister dataset: the corresponding corpus for the other pilot year at |
| [`G-reen/cc-re-2020`](https://huggingface.co/datasets/G-reen/cc-re-2020) / |
| [`G-reen/cc-re-2021`](https://huggingface.co/datasets/G-reen/cc-re-2021). |
|
|
| ## Pipeline |
|
|
| The pipeline composes the published recipes of FineWeb, GneissWeb, |
| Ultra-FineWeb, and WebOrganizer (extraction → filtering → dedup → annotation → |
| balanced selection). Full source code: the `ccre` pipeline repo (see |
| `reports/pipeline_config.yaml` in this repo for the exact configuration used). |
|
|
| 1. **Manifest** — enumerate the year's CommonCrawl dumps; sample WARC files |
| uniformly across crawls and segments (seeded, reproducible). |
| 2. **Extraction + heuristic filtering** (exact FineWeb stage-A recipe, via |
| [datatrove](https://github.com/huggingface/datatrove)): |
| WARC records (not WET) → UT1 adult/URL blocklist → trafilatura |
| (`favour_precision=True`, no comments) → fastText **lid.176 English ≥ 0.65** |
| → Gopher repetition filter → Gopher quality filter → C4 filters (without the |
| terminal-punctuation line rule) → FineWeb quality filters. |
| 3. **Cheap junk fences** (from GneissWeb, applied as lenient floors): |
| extreme-tokenized-document filter `0.1 < tokens/char < 0.5` |
| (catches mixed-script leakage, encoded junk, and permutation spam that |
| language ID misses); McAlpine-EFLAW readability is stored as **metadata |
| only** — never used as a filter, to preserve register diversity. |
| 4. **Quality floors, not ceilings** (Ultra-FineWeb + DCLM): every document is |
| scored by the [Ultra-FineWeb-en fastText classifier](https://huggingface.co/openbmb/Ultra-FineWeb-classifier) |
| (with its exact DeepSeek-V2-token preprocessing) and the |
| [DCLM fastText](https://huggingface.co/mlfoundations/fasttext-oh-eli5). |
| Keep rule: `dclm_score ≥ 0.002` (GneissWeb's permissive floor); the UFW score is recorded as metadata only (no UFW floor is applied — a UFW gate would skew the corpus toward educational text). These thresholds are |
| deliberately permissive **floors**: the goal is diverse, representative human |
| writing, not FineWeb-edu-style "educational" ceilings that would collapse the |
| register distribution a detector must learn. |
| 5. **Global per-year MinHash deduplication** (datatrove 4-stage MinHash: |
| signatures → buckets → clusters → filter), sha1 64-bit, |
| 14 buckets × 8 hashes over word |
| 5-grams (≈ 0.72 Jaccard threshold), |
| across **all** of the year's crawls (unlike FineWeb's per-dump dedup — |
| uniqueness matters more than benchmark-optimal mixing for detector data). |
| Duplicate-cluster sizes are retained as metadata. |
| 6. **Topic × format annotation** ([WebOrganizer](https://arxiv.org/abs/2502.10341)): |
| the URL-aware `TopicClassifier` and `FormatClassifier` (24 labels each, |
| gte-base-en-v1.5 backbone, max_length 512) label every |
| document; softmax confidences are stored. |
| 7. **Marginal-uniform selection** — the per-FORMAT and per-TOPIC row totals are |
| equalized as far as pool supply allows: water-filled marginal targets per |
| axis (labels whose entire eligible pool is below the common level contribute |
| everything they have; all other labels get an equal share), reconciled at |
| the topic × format cell level by an exact LP (HiGHS) minimizing total L1 |
| deviation from both axes' targets subject to per-cell pool capacities, down |
| to 500,000 rows; within-cell choice is `random` |
| (seeded). Achieved marginals ship in `reports/selection_report.json` |
| (`achieved_marginals`). Note the marginals are as-uniform-as-feasible, not |
| exactly uniform: scarce labels are supply-limited, and residual joint |
| infeasibility (a label's supply concentrated in saturated labels of the |
| other axis) is split across axes by the LP. |
| Before allocation, eligibility filters exclude non-prose and corrupted |
| rows: dropped topics ['Adult'], dropped formats ['Truncated', 'Content Listing', 'Structured Data'], |
| `language_score >= 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: `<crawl>/<warc-file#record>` | |
| | `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. |
|
|
