Initial Commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+data/afwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/arwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/astwiki.jsonl filter=lfs diff=lfs merge=lfs -text
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+data/bgwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/bnwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/cawiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/cewiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/cswiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/cywiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/dawiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/dewiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/elwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/enwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/eowiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/eswiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/etwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/euwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/fawiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/fiwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/frwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/glwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/hewiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/hiwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/hrwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/huwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/hywiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/idwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/itwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/jawiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/kawiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/kkwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/kowiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/lawiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/ltwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/lvwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/minwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/mkwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/mswiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/mywiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/nlwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/nnwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/nowiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/plwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/ptwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/rowiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/ruwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/shwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/simplewiki.jsonl filter=lfs diff=lfs merge=lfs -text
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+data/slwiki.jsonl filter=lfs diff=lfs merge=lfs -text
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+data/svwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/tawiki.jsonl filter=lfs diff=lfs merge=lfs -text
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+data/thwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/trwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/ukwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/urwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/uzwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/viwiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/zh_yuewiki.jsonl filter=lfs diff=lfs merge=lfs -text
+data/zhwiki.jsonl filter=lfs diff=lfs merge=lfs -text

DOCS.md

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+# Documentation
+
+This contains some *really* quick docs and notes on filtering with SuperWIKI NEXT.
+
+## wikipedia_soup.py
+
+...Is the main class that handles the bulk of the filtering.
+
+Each filter has code documentation to explain what each function generally does. So I'd suggest you to read those instead.
+
+### Usage for wikipedia_soup.py
+
+probably the most important bit.
+
+wikipedia_soup takes in `*.ndjson` files directly from html wikipedia dumps. via the `process-root` command.
+
+*Note: there are 3 publicly exposed commands via typer, `process-root`, `process-folder`, `process-file`*
+
+`process-root` is probably what you want to use. It takes in the following folder structure:
+
+```
+dumps <- Input folder for [process-root]
+ |-afwiki-NS0-20240420-ENTERPRISE-HTML <- Input folder for [process-folder]
+    |-afwiki_namespace_0_0.ndjson <- Input file for [process-file]
+    |-afwiki_namespace_0_1.ndjson
+    |-afwiki_namespace_0_2.ndjson
+    ...
+ |-arwiki-NS0-20240420-ENTERPRISE-HTML
+    |-arwiki_namespace_0_0.ndjson
+    |-arwiki_namespace_0_1.ndjson
+    |-arwiki_namespace_0_2.ndjson
+    ...
+ ... And so on...
+```
+
+Downloading and filtering the files is relatively easy.
+
+1. Get a list of http urls (Whichever way you prefer)
+2. Download said list (wget, curl, aria2c, etc)
+3. Extract tar files into their own folder as shown above
+4. Run `process-root` command.
+5. Patience.
+6. ???
+7. Finished!
+
+
+
+## wikipedia_template.py
+
+This file contains templates used in Wikipedia articles.
+
+If you do need to update a template, follow these steps:
+
+1. Open the file in your web browser.
+2. Paste the following URL, replacing `<ID>` with the relevant Wikidata entry ID.
+
+```
+https://www.wikidata.org/w/api.php?action=wbgetentities&ids=<ID>&format=json&props=labels
+```
+
+As for the related templates:
+
+- Stubs: `Q4663261`
+- Citation needed: `Q7106262`
+- Redirect: `Q6042392`
+
+**Note:** For Sections, there are currently no templates available. These must be added manually.
+
+## mediawiki_soup.py
+
+Before the introduction of Hugging Face's Datatrove and for the sake of simpler code development, this module implemented the `MediaWikiSoup` class.
+
+This class processes HTML content into markdown format and performs additional post-processing steps on the resulting markdown.
+
+`MediaWikiSoup` leverages a "filter chain" architecture. You can extend its functionalities by adding filter functions using either `add_markdown_filter` (for markdown processing) or `add_soup_filter` (for BeautifulSoup processing).
+
+## html2markdown.py
+
+Contains a customized markdownify instance. Since this is mainly carried over from 1.5, details on it are a bit hazy.
+
+For `<a>` elements, I only use the text contained. That is to say, I don't include the href.
+
+```html
+<a href="//example.com">This is an example</a>
+```
+
+Will be md'd into:
+
+```md
+This is an example
+```
+
+For image elements:
+
+```html
+<img src="//example.com" alt="Alt Text"/>
+```
+
+Will be md'd into:
+
+```md
+Alt Text
+```
+
+For `<li>` elements, I'm unsure what was the reason behind it. Now, God/LLM/Model/??? only knows.
+
+## folders2jsonl.py
+
+Is a simple script converting chunked ndjson files into 1 singular file for ease of processing.
+
+# Tools
+
+Extra tools unrelated to main filtering. But used in some shape or form.
+
+## tools/wikipedia_eligablewiki.py
+
+As the title says, it unbiasedly selects groups of wikipedia with high enough content. Refer to `Selection of Wikipedia` for how it was computed.
+
+the stats .json file can be fetched from the source page here: https://commons.wikimedia.org/w/index.php?title=Data:Wikipedia_statistics/data.tab&action=edit
+
+Copy the json in the source into a .json file and you should be good to go.
+
+if you have to filter a list of URLS, it should look like this:
+
+Using mirror.accum.se as mirror:
+```txt
+https://mirror.accum.se/mirror/wikimedia.org/other/enterprise_html/runs/20240420/amiwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+https://mirror.accum.se/mirror/wikimedia.org/other/enterprise_html/runs/20240420/amwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+https://mirror.accum.se/mirror/wikimedia.org/other/enterprise_html/runs/20240420/angwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+https://mirror.accum.se/mirror/wikimedia.org/other/enterprise_html/runs/20240420/anwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+```
+
+Or with the official dumps:
+```
+https://dumps.wikimedia.org/other/enterprise_html/runs/20240420/amiwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+https://dumps.wikimedia.org/other/enterprise_html/runs/20240420/amwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+https://dumps.wikimedia.org/other/enterprise_html/runs/20240420/angwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+https://dumps.wikimedia.org/other/enterprise_html/runs/20240420/anwiki-NS0-20240420-ENTERPRISE-HTML.json.tar.gz
+```
+
+## tools/wikipedia_pageview.py
+
+Not used in NEXT, but included. The idea is to accumulate all pageviews and filter each article based on pageviews. While it's a neat idea, I just didn't use it.
+
+## tools/wikipedia_mediaalias.py
+
+Pretty sure it's unfinished. I didn't use it in the end. Similar to pageview. Though someone could improvise and use it.

README.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- crowdsourced
+pretty_name: SuperWikiNEXT-32B
+paperswithcode_id: null
+license:
+- cc-by-sa-3.0
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- language-modeling
+- masked-language-modeling
+source_datasets:
+- original
+multilinguality:
+- multilingual
+language:
+- af
+- ar
+- ast
+- az
+- be
+- bg
+- bn
+- ca
+- ce
+- cs
+- cy
+- da
+- de
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- gl
+- he
+- hi
+- hr
+- hu
+- hy
+- id
+- it
+- ja
+- ka
+- kk
+- ko
+- la
+- lt
+- lv
+- mk
+- ms
+- my
+- nl
+- nn
+- 'no'
+- pl
+- pt
+- ro
+- ru
+- sh
+- sk
+- sl
+- sr
+- sv
+- ta
+- tg
+- th
+- tr
+- uk
+- ur
+- uz
+- vi
+- zh
+size_categories:
+- 10B<n<100B
+---
+
+# Dataset Card for SuperWikiNEXT-32B
+
+![](Waifu.png "Based off from Wikipe-tan (Maid, cyan hair, short hair) and Wikipedia's globe logo.")
+
+*Waifu to catch your attention.*
+
+## Dataset Details
+
+### Dataset Description
+
+*SuperWikipedia-NEXT* is an enhanced version of the SuperWIKI dataset. Which SuperWIKI was born out of the thought of a better filtered Wikipedia while retaining markdowns. 
+*SuperWikipedia-NEXT* contains **~32.44B** Tokens (llama-2-7b-chat-tokenizer) / **~27.92B** Tokens (RWKV Tokenizer) from approximately **60** "High quality" / "Selected" languages.
+
+- **Curated by:** KaraKaraWitch
+- **Funded by [optional]:** Recursal.ai (I work there lol)
+- **Shared by [optional]:** KaraKaraWitch
+- **Language(s) (NLP):** Many. Refer to the data below for a list of languages.
+- **License:** cc-by-sa-4.0, 
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Source Data:** [https://dumps.wikimedia.org/other/enterprise_html/](https://dumps.wikimedia.org/other/enterprise_html)
+
+### Dataset Summary
+
+Wikipedia dataset containing cleaned articles of all languages.
+The dataset is manually built from Wikipedia HTML dumps with each split for each language. 
+Each example contains the content of one full Wikipedia article.
+
+### Supported Tasks and Leaderboards
+
+The dataset is generally used for Language Modelling.
+
+### Languages
+
+We have selected the following Wikipedia's:
+
+```
+af.wikipedia.org
+ar.wikipedia.org
+ast.wikipedia.org
+az.wikipedia.org
+be.wikipedia.org
+bg.wikipedia.org
+bn.wikipedia.org
+ca.wikipedia.org
+ce.wikipedia.org
+cs.wikipedia.org
+cy.wikipedia.org
+da.wikipedia.org
+de.wikipedia.org
+el.wikipedia.org
+en.wikipedia.org
+eo.wikipedia.org
+es.wikipedia.org
+et.wikipedia.org
+eu.wikipedia.org
+fa.wikipedia.org
+fi.wikipedia.org
+fr.wikipedia.org
+gl.wikipedia.org
+he.wikipedia.org
+hi.wikipedia.org
+hr.wikipedia.org
+hu.wikipedia.org
+hy.wikipedia.org
+id.wikipedia.org
+it.wikipedia.org
+ja.wikipedia.org
+ka.wikipedia.org
+kk.wikipedia.org
+ko.wikipedia.org
+la.wikipedia.org
+lt.wikipedia.org
+lv.wikipedia.org
+min.wikipedia.org
+mk.wikipedia.org
+ms.wikipedia.org
+my.wikipedia.org
+nl.wikipedia.org
+nn.wikipedia.org
+no.wikipedia.org
+pl.wikipedia.org
+pt.wikipedia.org
+ro.wikipedia.org
+ru.wikipedia.org
+sh.wikipedia.org
+simple.wikipedia.org
+sk.wikipedia.org
+sl.wikipedia.org
+sr.wikipedia.org
+sv.wikipedia.org
+ta.wikipedia.org
+tg.wikipedia.org
+th.wikipedia.org
+tr.wikipedia.org
+uk.wikipedia.org
+ur.wikipedia.org
+uz.wikipedia.org
+vi.wikipedia.org
+zh-min-nan.wikipedia.org
+zh.wikipedia.org
+zh-yue.wikipedia.org
+```
+
+*`.wikipedia.org`* extensions have been added for your convenience.
+
+### Selection of Wikipedia
+
+We deem a particular Wikipedia language as high quality if:
+
+1. Has a total article count of `>100,000`.
+2. Has a `Depth > 5.1`.
+
+*Depth is calculated using the following equation:*
+
+`depth = (article_edits / total_pages) * ((total_pages - articles) / articles) ** 2`
+
+This formula is directly taken from [list of Wikipedias.](https://meta.wikimedia.org/wiki/Wikipedia_article_depth)
+
+### Filtering
+
+Extensive HTML and markdown filtering has been done to derive the final dataset.
+
+For HTML:
+
+1. Parse the article content with BeautifulSoup.
+2. We first extract out titles from the Soup. 
+3. Drop (As in, don't process / skip processing) *Stub articles.* To ensure multilanguage coverage, we use a list of stub names found across multiple languages using wikidata. (We have included the template names within `wikipedia_template.py`)
+4. Drop *Lsjbot* bot created articles.
+5. Collapse styles with `data-mw` component into its next sibling.
+6. Remove raw `href` links. (Text of href == href link)
+7. Remove citation needed Templates
+8. Remove citation Templates
+9. Remove Redirect Templates
+10. Drop articles where the article content consists of 50% or more of tables and lists.
+11. Remove message boxes. (Orange alert boxes on top of articles)
+12. Remove infoboxes boxes. (Infoboxes on the right)
+13. Selectively remove tables which consist of just empty spaces. (Number of `<td>` elements > len(text_size) and text_size < 50)
+14. Cleanup latex code.
+15. Empty `class` attributes and `data-mw` attributes
+
+For Markdown:
+
+1. Cleanup punctuations.
+2. Collect text length (normalized text to NKFC, keeping CJK characters as is while decomposing Arabic characters, Counting double width characters as 2 instead of 1, )
+3. Filter based on the collected text length (If the article is less than 1000 characters long, it is dropped.)
+
+The final Markdown text and additional data is included in the jsonl file. Additionally, the scripts used are located in the main directory of this folder as well.
+
+### Data keys
+
+Users can run `less` to see the contents. A sample and a list of dictionary keys have been provided below:
+
+```json
+{
+    "text": "\n**Tharman Shanmugaratnam** PBM (born 25 February 1957) is a Singaporean politician and economist. He is the President of Singapore since 2023. \n\nHe was Senior Minister of Singapore between 2019 and 2023. He was also the Coordinating Minister for Social Policies between 2015 and 2023, and Chairman of the Monetary Authority of Singapore between 2011 and 2023.\n\nOn 8 June 2023, Tharman announced his plans to run for president in the 2023 presidential election. He was elected on 2 September 2023 in a landslide victory, winning 70.40% of the vote.\n\nEarly life and education\n------------------------\n\nTharman was born in the Colony of Singapore in 1957. He studied at the Anglo-Chinese School. When he was studying there, he was not interested in his studies and was not disciplined. However, he liked to read and tried out poetry. During his time at Anglo-Chinese School, he created four poets with his schoolmates. Also, he was interested in sports and spent most of his time playing sports. He even joined his school's hockey team.\n\nThen, he attended the London School of Economics (LSE), graduating with a Bachelor of Science degree in economics.\n\nAfter getting his bachelor's, Tharman went on to study at Wolfson College at the University of Cambridge. There, he completed a Master of Philosophy degree in economics. \n\nTharman then became a student at the Harvard Kennedy School at Harvard University, where he finished a Master in Public Administration (MPA) degree. He was a student activist there. He explored left-wing politics, as he did not agree with the ruling People's Action Party back in Singapore.\n\nTharman was a recipient of the Lucius N. Littauer Fellows Award. The award is given to students with MPA's who showed academic excellence and leadership.In 2011, the LSE gave him an Honorary Fellowship.<...TRUNCATED IN SAMPLE>",
+    "meta": {
+        "title": "Tharman Shanmugaratnam",
+        "mostly_tablelist": false,
+        "tablelist_ratio": [
+            4082,
+            8644,
+            0.47223507635354
+        ],
+        "infobox": [
+            "<...TRUNCATED IN SAMPLE>"
+        ],
+        "td_tables": [],
+        "text_length": 5553
+    }
+}
+```
+
+```
+text: str (Markdown text)
+meta: dict (Contains additional metadata / meta)
+  - title: str (Article Title)
+  - mostly_tablelist: bool (Internal flag for HTML step 10)
+  - tablelist_ratio: list (Internal data, used to compute mostly_tablelist.)
+  - infobox: list (A list of extracted infoboxes with data-mw attribute for the raw html data.)
+  - td_tables: list (Extracted tables from HTML step 13)
+  - text_length: int (Obtained from markdown step 2)
+```
+
+### Dataset Curators
+
+KaraKaraWitch. (I typically hangout in PygmalionAI discord, sometimes EleutherAI. If something is wrong, `@karakarawitch` on discord.)
+
+I'd be happy if you could spread the word and recommend this dataset over wikitext for your use cases `:)`
+
+### Licensing Information
+
+Most of Wikipedia's text and many of its images are co-licensed under the
+[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
+(CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License)
+(GFDL) (un-versioned, with no invariant sections, front-cover texts, or back-cover texts). 
+
+Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
+text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
+the text.
+
+### Citation Information
+
+```
+@ONLINE{superwiki-next,
+  title         = {SuperWikiNEXT-32B},
+  author        = {KaraKaraWitch, recursal.ai},
+  year          = {2023},
+  howpublished  = {\url{https://huggingface.co/datasets/WitchesSocialStream/SuperWikipedia-NEXT}},
+}
+```

data/afwiki.jsonl

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+size 218484275

data/arwiki.jsonl

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