') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ', 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ', 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ', 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); })(); GitHub - WikiExtractor/wikiextractor: A tool for extracting plain text from Wikipedia dumps · GitHub
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WikiExtractor

WikiExtractor.py is a Python script that extracts and cleans text from a Wikipedia database backup dump, e.g. https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2 for English.

The tool is written in Python and requires Python 3 but no additional library. Warning: problems have been reported on Windows due to poor support for StringIO in the Python implementation on Windows.

For further information, see the Wiki.

Wikipedia Cirrus Extractor

cirrus-extractor.py is a version of the script that performs extraction from a Wikipedia Cirrus dump. Cirrus dumps contain text with already expanded templates.

Cirrus dumps are available at: cirrussearch.

Details

WikiExtractor performs template expansion by preprocessing the whole dump and extracting template definitions.

In order to speed up processing:

  • multiprocessing is used for dealing with articles in parallel
  • a cache is kept of parsed templates (only useful for repeated extractions).

Installation

The script may be invoked directly:

python -m wikiextractor.WikiExtractor <Wikipedia dump file>

It can also be installed from PyPi by doing:

pip install wikiextractor

or locally with:

(sudo) python setup.py install

The installer also installs two scripts for direct invocation:

wikiextractor (equivalent to python -m wikiextractor.WikiExtractor)
extractPage (to extract a single page from a dump)

Usage

Wikiextractor

The script is invoked with a Wikipedia dump file as an argument:

python -m wikiextractor.WikiExtractor <Wikipedia dump file> [--templates <extracted template file>]

The option --templates extracts the templates to a local file, which can be reloaded to reduce the time to perform extraction.

The output is stored in several files of similar size in a given directory. Each file will contains several documents in this document format.

usage: wikiextractor [-h] [-o OUTPUT] [-b n[KMG]] [-c] [--json] [--html] [-l] [-ns ns1,ns2]
[--templates TEMPLATES] [--no-templates] [--html-safe HTML_SAFE] [--processes PROCESSES]
[-q] [--debug] [-a] [-v]
input
Wikipedia Extractor:
Extracts and cleans text from a Wikipedia database dump and stores output in a
number of files of similar size in a given directory.
Each file will contain several documents in the format:
<doc id="" url="" title="">
...
</doc>
If the program is invoked with the --json flag, then each file will contain several documents formatted as json ojects, one per line, with the following structure
{"id": "", "revid": "", "url": "", "title": "", "text": "..."}
The program performs template expansion by preprocesssng the whole dump and
collecting template definitions.
positional arguments:
input XML wiki dump file
optional arguments:
-h, --help show this help message and exit
--processes PROCESSES
Number of processes to use (default 79)
Output:
-o OUTPUT, --output OUTPUT
directory for extracted files (or '-' for dumping to stdout)
-b n[KMG], --bytes n[KMG]
maximum bytes per output file (default 1M)
-c, --compress compress output files using bzip
--json write output in json format instead of the default <doc> format
Processing:
--html produce HTML output, subsumes --links
-l, --links preserve links
-ns ns1,ns2, --namespaces ns1,ns2
accepted namespaces
--templates TEMPLATES
use or create file containing templates
--no-templates Do not expand templates
--html-safe HTML_SAFE
use to produce HTML safe output within <doc>...</doc>
Special:
-q, --quiet suppress reporting progress info
--debug print debug info
-a, --article analyze a file containing a single article (debug option)
-v, --version print program version

Saving templates to a file will speed up performing extraction the next time, assuming template definitions have not changed.

Option --no-templates significantly speeds up the extractor, avoiding the cost of expanding MediaWiki templates.

For further information, visit the documentation.

Cirrus Extractor

usage: cirrus-extract.py [-h] [-o OUTPUT] [-b n[KMG]] [-c] [-ns ns1,ns2] [-q]
[-v]
input
Wikipedia Cirrus Extractor:
Extracts and cleans text from a Wikipedia Cirrus dump and stores output in a
number of files of similar size in a given directory.
Each file will contain several documents in the format:
<doc id="" url="" title="" language="" revision="">
...
</doc>
positional arguments:
input Cirrus Json wiki dump file
optional arguments:
-h, --help show this help message and exit
Output:
-o OUTPUT, --output OUTPUT
directory for extracted files (or '-' for dumping to
stdin)
-b n[KMG], --bytes n[KMG]
maximum bytes per output file (default 1M)
-c, --compress compress output files using bzip
Processing:
-ns ns1,ns2, --namespaces ns1,ns2
accepted namespaces
Special:
-q, --quiet suppress reporting progress info
-v, --version print program version

extractPage

Extract a single page from a Wikipedia dump file.

usage: extractPage [-h] [--id ID] [--template] [-v] input
Wikipedia Page Extractor:
Extracts a single page from a Wikipedia dump file.
positional arguments:
input XML wiki dump file
optional arguments:
-h, --help show this help message and exit
--id ID article number
--template template number
-v, --version print program version

License

The code is made available under the GNU Affero General Public License v3.0.

Reference

If you find this code useful, please refer it in publications as:

@misc{Wikiextractor2015,
author = {Giuseppe Attardi},
title = {WikiExtractor},
year = {2015},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/attardi/wikiextractor}}
}

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A tool for extracting plain text from Wikipedia dumps

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