open-index/open-wikipedia-text

Open Wikipedia (Text)

Every Wikipedia article as clean plain text, 139.4K articles across 1 languages

What is it?

This dataset contains every article from every language edition of Wikipedia, converted from raw MediaWiki markup into clean plain text. All formatting, templates, references, tables, HTML tags, and wiki syntax are stripped away, leaving only the readable content of each article.

The source data comes from the official Wikimedia database dumps. Each language's full XML export is streamed, parsed, and converted article by article. The results are stored as sharded Apache Parquet files with Zstandard compression, organized by language.

This is the plain text variant of the Open Wikipedia collection. If you need Markdown with preserved headings, bold, italic, code blocks, and links, see open-index/open-wikipedia-markdown. If you need the original MediaWiki source markup, see open-index/open-wikipedia.

139.4K articles | 1 languages | Last updated: 2026-04-03 | License: CC BY-SA 4.0

What is being released?

The dataset is organized as one directory per language, with sharded Parquet files inside each:

data/
  en/en-00000.parquet      English, shard 0
     en-00001.parquet      English, shard 1
     ...
  de/de-00000.parquet      German
  fr/fr-00000.parquet      French
  es/es-00000.parquet      Spanish
  ja/ja-00000.parquet      Japanese
  ...
  la/la-00000.parquet      Latin

Each Parquet file contains up to 500,000 rows. Languages with fewer articles fit in a single shard. All files use Zstandard compression.

How to download and use this dataset

Using DuckDB

DuckDB can read Parquet files directly from Hugging Face without downloading anything first.

-- Count articles per language
SELECT lang, COUNT(*) as articles
FROM read_parquet('hf://datasets/open-index/open-wikipedia-text/data/*/*.parquet')
GROUP BY lang
ORDER BY articles DESC;

-- Full-text search across all languages
SELECT title, lang, length, url
FROM read_parquet('hf://datasets/open-index/open-wikipedia-text/data/*/*.parquet')
WHERE text ILIKE '%artificial intelligence%'
ORDER BY length DESC
LIMIT 20;

-- Article length distribution for English
SELECT
    percentile_disc(0.25) WITHIN GROUP (ORDER BY length) AS p25,
    percentile_disc(0.50) WITHIN GROUP (ORDER BY length) AS p50,
    percentile_disc(0.75) WITHIN GROUP (ORDER BY length) AS p75,
    percentile_disc(0.90) WITHIN GROUP (ORDER BY length) AS p90,
    percentile_disc(0.99) WITHIN GROUP (ORDER BY length) AS p99,
    AVG(length)::INT AS avg_length,
    MAX(length) AS max_length
FROM read_parquet('hf://datasets/open-index/open-wikipedia-text/data/en/*.parquet');

-- Total text volume per language (in GB)
SELECT lang, COUNT(*) AS articles,
    ROUND(SUM(length) / 1e9, 2) AS text_gb
FROM read_parquet('hf://datasets/open-index/open-wikipedia-text/data/*/*.parquet')
GROUP BY lang
ORDER BY text_gb DESC
LIMIT 20;

-- Find the longest articles in each language
SELECT lang, title, length, url
FROM (
    SELECT *, ROW_NUMBER() OVER (PARTITION BY lang ORDER BY length DESC) AS rn
    FROM read_parquet('hf://datasets/open-index/open-wikipedia-text/data/*/*.parquet')
)
WHERE rn = 1
ORDER BY length DESC
LIMIT 20;

Using datasets

from datasets import load_dataset

# Load English Wikipedia
ds = load_dataset("open-index/open-wikipedia-text", "en")
print(ds["train"][0]["title"])
print(ds["train"][0]["text"][:500])

# Stream the full dataset without downloading everything
ds = load_dataset("open-index/open-wikipedia-text", "en", split="train", streaming=True)
for item in ds:
    print(item["title"], item["length"])

# Load a specific language
ds = load_dataset("open-index/open-wikipedia-text", "de")
print(f"German articles: {len(ds['train']):,}")

Using huggingface_hub

from huggingface_hub import snapshot_download

# Download only English
snapshot_download(
    "open-index/open-wikipedia-text",
    repo_type="dataset",
    local_dir="./wiki-text/",
    allow_patterns="data/en/*",
)

For faster downloads, install pip install huggingface_hub[hf_transfer] and set HF_HUB_ENABLE_HF_TRANSFER=1.

Using the CLI

# Download a single language
huggingface-cli download open-index/open-wikipedia-text \
    --include "data/la/*" \
    --repo-type dataset --local-dir ./wiki-text/

Sentence embeddings

Plain text is the ideal input for embedding models. Here is a quick example:

from datasets import load_dataset
from sentence_transformers import SentenceTransformer

ds = load_dataset("open-index/open-wikipedia-text", "en", split="train")
model = SentenceTransformer("all-MiniLM-L6-v2")

# Embed the first 1000 article texts (truncated to 512 chars)
texts = [row["text"][:512] for row in ds.select(range(1000))]
embeddings = model.encode(texts, show_progress_bar=True)
print(f"Embeddings shape: {embeddings.shape}")

Dataset statistics

Languages

Language	Code	Articles	Shards
Latin	la	139.4K	1

Schema

Every Parquet file shares the same schema:

Column	Type	Description
id	int64	Wikipedia page ID, unique within each language edition
title	string	Article title as it appears on Wikipedia
text	string	Full article body as plain text, all markup removed
url	string	Direct URL to the Wikipedia article
lang	string	ISO 639 language code (e.g. en, de, fr, ja)
length	int32	Plain text body length in bytes
timestamp	string	Last revision timestamp in ISO 8601 format

Example instance

Here is an example row from the English partition:

{
  "id": 12,
  "title": "Anarchism",
  "text": "Anarchism is a political philosophy and movement that is against all forms of authority and seeks to abolish...",
  "url": "https://en.wikipedia.org/wiki/Anarchism",
  "lang": "en",
  "length": 65210,
  "timestamp": "2025-12-15T08:22:01Z"
}

The text field contains only readable content. All wiki syntax, HTML tags, templates, references, and formatting markers have been removed.

What gets stripped

Every element of MediaWiki markup is removed to produce clean plain text:

Element	Handling
{{templates}}	Removed entirely, including Infobox, Navbox, Taxobox, and all other templates
`{	tables
<ref> citations	Removed, including named references
[[wiki links]]	Replaced with the display text only (no URL, no brackets)
'''bold''' / ''italic''	Replaced with the plain text content
== Headings ==	Replaced with the heading text only
<!-- comments -->	Removed
[[File:]] / [[Image:]]	Removed
[[Category:]]	Removed
<code>, <pre>, <syntaxhighlight>	Tag markup removed, content preserved as plain text
All other HTML tags	Removed
Magic words	__NOTOC__, __FORCETOC__, and similar directives are removed

How it works

The pipeline processes all 1 Wikipedia language editions through the following steps:

1. Download. The latest {lang}wiki-latest-pages-articles.xml.bz2 dump is streamed from dumps.wikimedia.org. Downloads support HTTP range resumption, so interrupted transfers pick up where they left off.

2. Parse. A streaming XML parser processes the bzip2-compressed dump without extracting it to disk. Only namespace-0 pages (articles) are kept. Redirects, talk pages, user pages, and all other namespaces are skipped.

3. Strip. Each article's wikitext is processed to remove all markup, templates, tables, references, HTML, and formatting. Wiki links are replaced with their display text. The result is clean, readable plain text.

4. Filter. Articles shorter than 100 bytes after conversion are excluded. This removes stubs, disambiguation pages, and other pages with minimal content.

5. Shard. Articles are written to Zstandard-compressed Parquet files, approximately 500,000 rows per shard. Multiple languages are processed in parallel using a worker pool.

6. Publish. Each language's shards are committed to this Hugging Face repository as they complete.

Considerations

Why plain text?

Plain text is the right choice when you need clean input without any formatting noise. Common use cases include:

• Text embeddings. Embedding models work best with pure text. Markdown markers like ** and ## add noise that can degrade similarity scores.
• Text classification. Topic classifiers and sentiment models typically expect plain text input.
• BM25 and keyword search. Traditional information retrieval benefits from clean text without markup artifacts.
• Tokenization and vocabulary analysis. Formatting symbols consume tokens without adding semantic value.

If you need document structure (headings, bold, code blocks, links), use the Markdown variant instead.

Known limitations

• Conversion is regex-based, not a full parser. Some complex wikitext constructs may leave small artifacts. The vast majority of articles convert cleanly.
• Templates are stripped, not expanded. Infoboxes and navigation templates are removed entirely rather than converted to their rendered text output.
• One snapshot in time. This dataset represents a single snapshot of each language's dump. It does not track edit history.
• Dump availability varies. Not all language editions have their dumps available at all times.

Related datasets

• open-index/open-wikipedia-markdown - Same articles with Markdown formatting preserved. Headings, bold, italic, code blocks, and links are retained.
• open-index/open-wikipedia - Same articles in original MediaWiki wikitext markup with templates, tables, references, and all source elements.

Thanks

The content in this dataset was written by millions of Wikipedia editors worldwide and is hosted by the Wikimedia Foundation. The raw data comes from the Wikimedia database dumps, which the Foundation makes freely available for download.

Wikipedia is one of humanity's greatest collaborative achievements. All credit for the content goes to the volunteer editors who write, review, and maintain it.

This dataset is an independent conversion and is not affiliated with or endorsed by the Wikimedia Foundation.

Licensing

Wikipedia content is released under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). This dataset inherits that license. If you redistribute or build upon this data, you must give appropriate credit and share your contributions under the same license.

Citation

@dataset{open_wikipedia_text,
  title     = {Open Wikipedia (Text)},
  author    = {Open Index},
  year      = {2026},
  url       = {https://huggingface.co/datasets/open-index/open-wikipedia-text},
  license   = {CC BY-SA 4.0},
  publisher = {Hugging Face}
}

Last updated: 2026-04-03