--- license: other license_name: odatl-1.0 language: - en - de - fr - ru - es - it - ja - zh - pt - ar - fa - tr - pl - nl - id - ko - vi - uk - ca - hu - fi - cs - ro - sv size_categories: - 1M **Live status:** batch 4 scraping (2.5 min in, ~3.1 GB raw, ~748k docs). > The Colab scraper runs continuously: ~10.6 GiB raw per batch → losslessly zipped > (ZIP_DEFLATED, level 6, allowZip64), uploaded immediately via `huggingface_hub`, > with a manifest for cross-VM resume. ## Sources - **Wikipedia** — official `wikimedia/wikipedia` `20231101` parquet shards (HF mirror of Wikimedia dumps); mirror `mirror.accum.se` as fallback. Multi-language priority queue: de, fr, ru, es, it, ja, zh, pt, ar, fa, tr, pl, nl, id, ko, vi … (300+ configs; enwiki is only one shard of the total). - **Internet Archive** — `collection:gutenberg` (public-domain plain-text books) via `advancedsearch` + `metadata` + `archive.org/download` — polite `IA_CONCURRENCY=20`, per-host pacing, robots-friendly. Both pipelines emit the same JSONL record format and feed the same batch/zip loop. ## Record schema (JSONL, one object per line) ```json {"src": "wiki", "url": "https://...", "title": "...", "chars": 1234, "text": "..."} {"src": "ia", "url": "https://archive.org/details/...", "title": "...", "chars": 1234, "text": "..."} ``` - `src` — `wiki` or `ia` - `url` — canonical article/item URL - `title` — title trimmed - `chars` — character count of `text` - `text` — cleaned plain-text body ## Cleaning pipeline 1. Segmented parallel HTTP of source shards (`SEGMENTS=10`, `SEGMENTS=10` for parquet, 8 concurrent `archive.org` fetches) 2. Multi-process (2× fork) decode: `pyarrow`/`orjson` for parquet, streaming `bz2` for legacy dumps, plain-text for IA 3. Per-record `clean_wiki` / IA Gutenberg-header strip: - Gutenberg header/footer carve (`*START…*END*`), tab/NBSP→space, CRLF normalization, control-char strip, blank-run collapse, email redaction (`[email]`) - Min-length gates: `MIN_TEXT_LEN=300`, `MIN_DOC=280` - Exact-duplicate removal per batch (title+URL `blake2b-12`) 4. Batch-shard JSONL → ZIP (deflated level 6, `allowZip64`), immediate `HfApi.upload_file` + manifest append + manifest re-upload for resume All ZIPs are **lossless** (`ZIP_DEFLATED` is LZ77+Huffman — perfectly reversible). ## Provenance & attribution - Underlying Wikipedia articles © their contributors, dual-licensed CC-BY-SA/GFDL. - Underlying IA Gutenberg texts are public domain (U.S.). - This compilation and its cleaned artifacts are additionally distributed under **ODATL-1.0** (below). Users must preserve *both* upstream and compilation attribution. ## Reproducibility The full scraper (`scraper_v3.py`) runs entirely on Google Colab (CPU-only). State is kept in a `manifest.jsonl` on the HF repo itself; a new VM resumes by skipping `done_keys` (shard URLs + `ia:`). Progress and logs live in `/content/data/progress.json` & `scraper.log`. ## License This dataset is released under the **Open Data Attribution Training Disclosure License (ODATL-1.0)**, reproduced in full below. --- # Open Data Attribution Training Disclosure License (ODATL-1.0) Version 1.0 — July 2026 A permissive open-data license requiring attribution and mandatory disclosure of AI training use. ## SECTION 1 — DEFINITIONS **1.1** "Dataset" refers to the collection of data, files, metadata, annotations, structures, or any other materials distributed under this License, including any updates, subsets, or modified versions. **1.2** "Licensor" refers to the entity or individual who releases the Dataset under this License. **1.3** "Licensee" refers to any person, organization, or system that accesses, uses, modifies, redistributes, or incorporates the Dataset. **1.4** "Model" refers to any machine learning system, artificial intelligence system, algorithm, statistical model, or computational process trained, fine-tuned, evaluated, or otherwise developed using the Dataset. **1.5** "Public Notice" refers to a clear, visible, and publicly accessible statement acknowledging use of the Dataset. **1.6** "Derivative Dataset" refers to any dataset created by modifying, transforming, filtering, augmenting, or otherwise altering the original Dataset. **1.7** "Derivative Model" refers to any Model whose training data includes the Dataset or any Derivative Dataset. ## SECTION 2 — GRANT OF RIGHTS **2.1** The Licensor grants the Licensee a worldwide, royalty-free, non-exclusive, irrevocable permission to: - Use, copy, and redistribute the Dataset for any purpose. - Modify, transform, or build upon the Dataset. - Create Derivative Datasets. - Train, fine-tune, evaluate, or otherwise use the Dataset for machine learning or AI development. - Create Derivative Models based on the Dataset. **2.2** These rights are intended to be compatible with open-source and open-data principles, including but not limited to CC-BY, ODC-BY, and other permissive data licenses. **2.3** No patent rights are granted or implied by this License. ## SECTION 3 — MANDATORY ATTRIBUTION **3.1** Any public use, redistribution, publication, or derivative work involving the Dataset must include the following attribution: > "This work uses data provided under the Open Data Attribution Training Disclosure License (ODATL-1.0)." **3.2** Attribution must appear in: - Documentation - Research papers - Model cards - Public datasets - Product descriptions - Any public-facing material referencing the Dataset **3.3** Attribution must remain intact and may not be removed, obscured, or altered. ## SECTION 4 — MANDATORY TRAINING DISCLOSURE **4.1** If the Dataset is used to train, fine-tune, evaluate, or otherwise develop any Model, the Licensee must provide a Public Notice stating: > "This model was trained using data provided under the Open Data Attribution Training Disclosure License (ODATL-1.0)." **4.2** This disclosure must appear in: - Model cards - Public releases of the Model - Research publications - Technical documentation - Product descriptions - Any public announcement or description of the Model **4.3** Disclosure must be truthful, visible, and accessible to the general public. **4.4** Failure to provide this disclosure immediately terminates all rights granted under this License. ## SECTION 5 — REDISTRIBUTION REQUIREMENTS **5.1** If the Licensee redistributes the Dataset or any Derivative Dataset, the Licensee must: - Include this License in full. - Clearly indicate any modifications made. - Preserve all attribution and disclosure requirements. **5.2** Derivative Datasets may be dual-licensed under other open-data licenses, provided this License remains included and enforceable. ## SECTION 6 — OPEN-SOURCE COMPATIBILITY **6.1** This License is designed to be compatible with: - Open Data Commons Attribution (ODC-BY) - Creative Commons Attribution (CC-BY) - Open-source AI research workflows - Open-data distribution platforms **6.2** Redistribution through open-source repositories (e.g., GitHub, HuggingFace, Kaggle) is permitted and encouraged. **6.3** Licensees may combine the Dataset with other open datasets, provided attribution and training disclosure obligations remain intact. ## SECTION 7 — PROHIBITED USES **7.1** The Licensee may not: - Claim exclusive ownership of the Dataset. - Remove or alter attribution or disclosure requirements. - Use the Dataset in violation of applicable laws or regulations. - Misrepresent the origin, nature, or licensing of the Dataset. **7.2** The Licensee may not apply technical or legal measures that restrict others from exercising rights granted under this License. ## SECTION 8 — NO WARRANTY **8.1** The Dataset is provided "as-is," without warranty of any kind, express or implied. **8.2** The Licensor is not liable for any damages, losses, or claims arising from use of the Dataset or any Derivative Model. ## SECTION 9 — TERMINATION **9.1** Rights under this License automatically terminate if the Licensee: - Fails to provide required attribution. - Fails to provide required training disclosure. - Violates any other term of this License. **9.2** Rights may be reinstated upon correction of the violation, unless the Licensor explicitly revokes permission. ## SECTION 10 — ACCEPTANCE **10.1** By accessing or using the Dataset, the Licensee agrees to be bound by the terms of this License. **10.2** Continued use of the Dataset constitutes ongoing acceptance of all terms. ## SECTION 11 — CONTACT **11.1** For permissions beyond this License, contact the Licensor.