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README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+configs:
+- config_name: articles
+  default: true
+  data_files: data/articles/*.parquet
+- config_name: chunks
+  data_files: data/chunks/*.parquet
+---
+
+# Simple English Wikipedia as clean md
+
+This dataset is a cleaned, structurally faithful approximation of the Simple English Wikipedia article corpus in Answer.AI's canonical `md` dialect. It was produced from the Wikimedia dump dated **20260801** by [Answer.AI's parse-wiki pipeline](https://github.com/AnswerDotAI/parse-wiki).
+
+It is designed for language-model training and for agent/RAG systems. The `articles` configuration provides complete documents for continued pretraining, corpus analysis, rechunking, and task-specific dataset creation. The `chunks` configuration provides section-aware retrieval units without duplicating article text: every chunk is a UTF-8 byte range into an article plus a small Markdown heading prefix. This keeps the corpus compact while preserving useful article and section breadcrumbs.
+
+## Contents
+
+- **articles** — 268,871 cleaned articles with dense `article_id`, Wikimedia page and revision IDs, title, revision timestamp, complete `md`, and conversion warnings.
+- **chunks** — 306,241 chunk indexes targeting about 700 words, with `article_id`, per-article `chunk_id`, UTF-8 `start_byte`/`byte_len`, `prefix_md`, `start_kind`, and `word_count`.
+
+The article row number equals `article_id`, and chunk rows are ordered by `article_id` then `chunk_id`. UTF-8 byte offsets—not Python character offsets—are used so ranges are stable across languages and tools.
+
+```python
+from datasets import load_dataset
+
+articles = load_dataset("answerdotai/simplewiki", "articles", split="train")
+chunks = load_dataset("answerdotai/simplewiki", "chunks", split="train")
+
+def materialize(article, chunk):
+    body = article["md"].encode()
+    start = chunk["start_byte"]
+    return chunk["prefix_md"] + body[start:start + chunk["byte_len"]].decode()
+
+chunk = chunks[100]
+text = materialize(articles[chunk["article_id"]], chunk)
+```
+
+`materialize.py` contains the same dependency-free reconstruction helper. Retrieval systems can store chunk rows in their vector index, fetch the corresponding article by dense row ID, and reconstruct only selected chunks. Training pipelines can stream complete article rows or materialize chunk rows on demand.
+
+## Processing
+
+The Rust pipeline parses wikitext directly into mdhtml's document model, applies Wikipedia-specific cleanup structurally, serializes each retained article once, and computes chunks from the same document without reparsing. It keeps namespace-0, non-redirect, non-disambiguation pages with at least 250 source bytes and discards pages without enough prose after cleanup. Navigation, maintenance, citation, sidebar, and similar non-article templates are removed using Wikimedia template/category metadata; common semantic templates, math, tables, and links are converted directly. Unsupported expansion-dependent constructs may remain as explicit raw wikitext islands.
+
+The chunker prefers high-level heading boundaries and only falls back to lower headings and block boundaries as needed. Footnote references and definitions are removed by this Wikipedia cleanup; the generic chunker also never duplicates footnote definitions across chunks.
+
+See `manifest.json` for exact input checksums, schema version, processing parameters, output checksums, and tool source identifiers.
+
+## Uses and limitations
+
+Useful applications include language-model pretraining or adaptation, embedding and retrieval corpora, grounded agent knowledge stores, search, summarization, and experiments with alternative chunking strategies. `page_id`, `revision_id`, and `timestamp` support provenance, exact revision links, auditing, and future incremental updates.
+
+This is intentionally a fast, useful approximation rather than a browser-perfect rendering. MediaWiki templates requiring server-side expansion may be simplified, removed, or retained as raw syntax. Images and Wikipedia-only presentation/navigation are generally omitted. Consumers requiring exact current facts should follow the stored revision provenance and compare against Wikimedia.
+
+## License and attribution
+
+Wikipedia text is provided by its contributors under the [Creative Commons Attribution-ShareAlike 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/) and may also be available under the GFDL. This transformed dataset is distributed under CC BY-SA 4.0. Attribute **Simple English Wikipedia contributors**, link to the relevant article or revision, and indicate that the text was transformed by Answer.AI's parse-wiki pipeline. For a row, an exact revision URL is:
+
+```python
+url = f"https://simple.wikipedia.org/w/index.php?oldid={article['revision_id']}"
+```
+
+See [Wikimedia's reuse guidance](https://foundation.wikimedia.org/wiki/Policy:Terms_of_Use) for full terms.

data/articles/train-00000.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0cd8029d2f7fc1db61ddd220a53c1bceab3880717fa31918ffda275730d5378
+size 189921335

data/chunks/train-00000.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3746de9d9cdd45eeebaf81198429ba098dc976647f1ef24203c3605646ae4821
+size 5722492

manifest.json

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+{
+  "schema_version": 1,
+  "wiki": "simplewiki",
+  "dump_date": "20260801",
+  "generated_at": "2026-08-24T05:29:28.699501+00:00",
+  "parameters": {
+    "min_bytes": 250,
+    "target_words": 700,
+    "limit": null
+  },
+  "counts": {
+    "articles": 268871,
+    "chunks": 306241
+  },
+  "unresolved_templates": [
+    [
+      "mp",
+      172171
+    ],
+    [
+      "gbmappingsmall",
+      43625
+    ],
+    [
+      "coloredlink",
+      40673
+    ],
+    [
+      "goal",
+      36612
+    ],
+    [
+      "flagicon",
+      23927
+    ],
+    [
+      "momp",
+      21352
+    ],
+    [
+      "mesh number",
+      20318
+    ],
+    [
+      "italictitle",
+      9970
+    ],
+    [
+      "in lang",
+      8536
+    ],
+    [
+      "flag",
+      8112
+    ],
+    [
+      "football box collapsible",
+      7912
+    ],
+    [
+      "fs player",
+      7320
+    ],
+    [
+      "nom",
+      7123
+    ],
+    [
+      "start date",
+      6894
+    ],
+    [
+      "nihongo",
+      6332
+    ],
+    [
+      "hlist",
+      6284
+    ],
+    [
+      "won",
+      5118
+    ],
+    [
+      "football player club statistics 2",
+      4941
+    ],
+    [
+      "fbaicon",
+      4471
+    ],
+    [
+      "football box",
+      4217
+    ],
+    [
+      "yes",
+      4212
+    ],
+    [
+      "party color|conservative party (uk)",
+      3988
+    ],
+    [
+      "no",
+      3525
+    ],
+    [
+      "center|\u2014",
+      3515
+    ],
+    [
+      "party color|labour party (uk)",
+      3445
+    ],
+    [
+      "fbu",
+      3382
+    ],
+    [
+      "sort dash",
+      3350
+    ],
+    [
+      "fb",
+      3345
+    ],
+    [
+      "fbicon",
+      3216
+    ],
+    [
+      "track listing",
+      3045
+    ]
+  ],
+  "raw_formats": [
+    [
+      "wikitext",
+      57986
+    ],
+    [
+      "html",
+      12746
+    ]
+  ],
+  "inputs": {
+    "xml": {
+      "path": "simplewiki-latest-pages-articles.xml",
+      "sha256": "cb7c2d5de57ed0f3aa40f6c8cf96a7ed365a5388d205da6b614df7c4e430cf29"
+    },
+    "page_props": {
+      "path": "simplewiki-latest-page_props.sql",
+      "sha256": "2820009b359ebbbea97a49a27b17c3906185b05e0e8c7f88fb30374f0d948f5a"
+    },
+    "template_db": {
+      "path": "simplewiki-templates.sqlite",
+      "sha256": "b0c5ac4f446c67dc4ec4590c5921af4093fc12c30aa34088def88129efb1120f"
+    }
+  },
+  "tools": {
+    "parse_wiki": {
+      "commit": "bc35219afe3739c81718424019649114afc18930",
+      "dirty": true
+    },
+    "mdhtml": {
+      "commit": "dfeaf6f61cfa54afbf16b718169bc3a6995f9373",
+      "dirty": true
+    }
+  },
+  "files": {
+    "data/articles/train-00000.parquet": {
+      "bytes": 189921335,
+      "sha256": "b0cd8029d2f7fc1db61ddd220a53c1bceab3880717fa31918ffda275730d5378"
+    },
+    "data/chunks/train-00000.parquet": {
+      "bytes": 5722492,
+      "sha256": "3746de9d9cdd45eeebaf81198429ba098dc976647f1ef24203c3605646ae4821"
+    }
+  }
+}

materialize.py

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+"""Reconstruct chunk text from the two dataset configurations."""
+
+def materialize(article, chunk):
+    body = article["md"].encode()
+    start = chunk["start_byte"]
+    return chunk["prefix_md"] + body[start:start+chunk["byte_len"]].decode()