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Datasets:
Gugu8
/
Scraped-Data

Tasks:
Text Generation
Feature Extraction
Modalities:
Text
Formats:
json
Languages:
English
German
French
Size:
10M - 100M
Tags:
wikipedia
corpus
cleaned
pretraining
internet-archive
gutenberg
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
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metadata 
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<n<10M
task_categories:
  - text-generation
  - feature-extraction
tags:
  - wikipedia
  - corpus
  - cleaned
  - pretraining
  - internet-archive
  - gutenberg
pretty_name: Scraped-Data — 1TB in Progress

Scraped-Data — Building to 1 TB

A continuously-growing, cleaned text corpus scraped, parsed, cleaned, losslessly-zipped, and auto-pushed from a CPU-only Google Colab pipeline.

Current repo size: ~26 GB → target: 1 TB (incremental batch uploads).

Current files

File Size Contents
corpus_batch1.clean.zip 4.80 GB ~2.9M Wikipedia docs (stored, from first scrape)
corpus_batch2.clean.zip 4.85 GB ~2.5M docs
corpus_batch3.clean.zip 4.91 GB ~2.3M docs
corpus_batch1.v3.zip 4.37 GB de-wiki shards, deflate-level-6
corpus_batch2.v3.zip 3.74 GB fr + ru shards
corpus_batch3.v3.zip 3.45 GB es + ru shards
manifest.jsonl — durable dedup ledger (zip → bytes → source keys)

Next batches are pushed live as they finish. Total so far: 26.1 GB compressed, ~8.8M+ records (+ additional Gutenberg/IA batches incoming).

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) 

{"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:<id>). 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.