CleanedWiki-jp

CleanedWiki-jp is a cleaned Japanese Wikipedia dataset prepared for LLM pre-training. It is built from Japanese Wikipedia article HTML, converted into Markdown, filtered for trainability.

The dataset keeps useful article structure instead of flattening everything into plain text. Suitable body tables are preserved as Markdown tables, and mathematical expressions are preserved in TeX form. Each row also includes a predicted Nippon Decimal Classification (NDC) category and a jReadability difficulty level, allowing users to rebalance training mixtures by domain and reading difficulty.

Dataset Features

• Covers cleaned Japanese Wikipedia article text from HTML sources.
• Preserves section headings, paragraphs, lists, and suitable tables in Markdown.
• Preserves math expressions as TeX.
• Removes non-training material such as references, external links, navigation-like content, infoboxes, noisy tables, unsuitable titles, and low-quality text.
• Adds NDC metadata predicted with the National Diet Library NDC Predictor: https://lab.ndl.go.jp/service/ndc_predictor/
• Adds a jReadability difficulty level from 1 to 6 for each retained article.
• Provides NDC-based subsets so downstream users can adjust the mixture ratio of broad topic categories.

Columns

Column	Type	Description
id	string	Wikipedia article identifier.
url	string	Original Japanese Wikipedia article URL.
title	string	Article title.
ndc_code	string	Two-digit predicted NDC code, or unknown when no category is available.
ndc_category	string	Japanese NDC category label with the code in parentheses.
ndc_confidence	float32	Confidence score returned by the NDC classifier.
gpt2_token_count	int64	Number of GPT-2 BPE tokens in text.
jreadability_level	int64	jReadability level from 1 to 6. Lower values indicate more difficult text, and higher values indicate easier text.
text	string	Cleaned Markdown text for pre-training.

jReadability Levels

Value	Level
1	Advanced second half
2	Advanced first half
3	Intermediate second half
4	Intermediate first half
5	Beginner second half
6	Beginner first half

Subsets

Subsets are defined from the first digit of the two-digit ndc_code. For example, 40, 41, and 49 are grouped into natural_sciences, while 70 through 79 are grouped into arts. The all subset contains every trainable article regardless of NDC category.

Config	NDC range	Contents	Rows	GPT-2 tokens
all	all	All trainable cleaned Japanese Wikipedia articles.	900,881	2,218,625,414
general_works	00-09	General works, libraries, bibliography, encyclopedias, journalism, and collections.	7,780	17,968,675
philosophy	10-19	Philosophy, psychology, ethics, religion, Buddhism, and Christianity.	17,040	35,194,421
history	20-29	History, regional histories, biography, geography, travel, and topography.	113,750	222,295,472
social_sciences	30-39	Politics, law, economics, society, education, folklore, and military affairs.	210,909	642,873,483
natural_sciences	40-49	Mathematics, physics, chemistry, astronomy, earth science, biology, medicine, and pharmacy.	51,651	114,787,471
technology	50-59	Engineering, construction, architecture, machinery, electrical engineering, manufacturing, chemical industry, and home economics.	92,681	246,839,362
industry	60-69	Agriculture, horticulture, animal industries, forestry, fisheries, commerce, transportation, and communications.	88,928	218,534,066
arts	70-79	Fine arts, painting, photography, crafts, music, theater, film, sports, and games.	260,123	573,058,364
language	80-89	Japanese, Chinese and other East Asian languages, English, European languages, and other languages.	3,654	8,478,351
literature	90-99	Japanese literature, Chinese and other East Asian literature, English and American literature, and European literatures.	54,365	138,595,749

Filtering Pipeline

The dataset is produced with the following pipeline:

1. Select the latest revision for each article identifier.
2. Reject unsuitable titles such as lists, disambiguation pages, redirects, and slash-based subpages.
3. Remove unwanted Wikipedia sections, including references, notes, external links, related items, bibliography, and navigation-like appendices.
4. Remove unwanted HTML classes and tags such as infoboxes, navboxes, reference markup, scripts, styles, images, and other non-body elements.
5. Convert MediaWiki math wrappers into canonical TeX before Markdown conversion.
6. Remove unsuitable tables while preserving body tables that contain useful prose.
7. Extract section headings, paragraphs, lists, and tables from HTML and convert them to Markdown.
8. Remove empty section headings and leading reading parentheses.
9. Normalize Unicode with NFKC and normalize whitespace, reference marks, and paragraph boundaries.
10. Reject text that is too short, has too few paragraphs or sentences, has low Japanese character ratio, has excessive symbols or list content, contains URLs or wikitext fragments, or appears to be disambiguation/reference-only prose.
11. Remove exact duplicate cleaned texts.
12. Compute the jReadability difficulty level for each retained article.
13. Predict the NDC category for each retained article.
14. Count GPT-2 BPE tokens on the final cleaned text.

Usage

from datasets import load_dataset

dataset = load_dataset("MK0727/CleanedWiki-jp", "all", split="train")
natural_sciences = load_dataset("MK0727/CleanedWiki-jp", "natural_sciences", split="train")

License

This dataset is derived from Japanese Wikipedia. The original text is contributed by Wikimedia contributors and is available under CC BY-SA 4.0 and the GNU Free Documentation License, subject to Wikimedia's licensing terms.

This cleaned derivative dataset is distributed under CC BY-SA 4.0. Reusers must comply with the attribution and share-alike requirements of the source license.