Datasets:
Multilinguality:
multilingual
Language Creators:
crowdsourced
Annotations Creators:
no-annotation
Source Datasets:
original
License:
metadata
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
pretty_name: Wikipedia
paperswithcode_id: null
licenses:
- cc-by-sa-3-0
- gfdl-1-3-or-later
task_categories:
- sequence-modeling
task_ids:
- language-modeling
source_datasets:
- original
multilinguality:
- multilingual
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
languages:
20200501-aa:
- aa
20200501-ab:
- ab
20200501-ace:
- ace
20200501-ady:
- unknown
20200501-af:
- af
20200501-ak:
- ak
20200501-als:
- als
20200501-am:
- am
20200501-an:
- an
20200501-ang:
- ang
20200501-ar:
- ar
20200501-arc:
- arc
20200501-arz:
- arz
20200501-as:
- as
20200501-ast:
- ast
20200501-atj:
- atj
20200501-av:
- av
20200501-ay:
- ay
20200501-az:
- az
20200501-azb:
- azb
20200501-ba:
- ba
20200501-bar:
- bar
20200501-bat-smg:
- sgs
20200501-bcl:
- bcl
20200501-be:
- be
20200501-be-x-old:
- unknown
20200501-bg:
- bg
20200501-bh:
- bh
20200501-bi:
- bi
20200501-bjn:
- bjn
20200501-bm:
- bm
20200501-bn:
- bn
20200501-bo:
- bo
20200501-bpy:
- bpy
20200501-br:
- br
20200501-bs:
- bs
20200501-bug:
- bug
20200501-bxr:
- bxr
20200501-ca:
- ca
20200501-cbk-zam:
- cbk
20200501-cdo:
- cdo
20200501-ce:
- ce
20200501-ceb:
- ceb
20200501-ch:
- ch
20200501-cho:
- cho
20200501-chr:
- chr
20200501-chy:
- chy
20200501-ckb:
- ckb
20200501-co:
- co
20200501-cr:
- cr
20200501-crh:
- crh
20200501-cs:
- cs
20200501-csb:
- csb
20200501-cu:
- cu
20200501-cv:
- cv
20200501-cy:
- cy
20200501-da:
- da
20200501-de:
- de
20200501-din:
- din
20200501-diq:
- diq
20200501-dsb:
- dsb
20200501-dty:
- dty
20200501-dv:
- dv
20200501-dz:
- dz
20200501-ee:
- ee
20200501-el:
- el
20200501-eml:
- eml
20200501-en:
- en
20200501-eo:
- eo
20200501-es:
- es
20200501-et:
- et
20200501-eu:
- eu
20200501-ext:
- ext
20200501-fa:
- fa
20200501-ff:
- ff
20200501-fi:
- fi
20200501-fiu-vro:
- vro
20200501-fj:
- fj
20200501-fo:
- fo
20200501-fr:
- fr
20200501-frp:
- frp
20200501-frr:
- frr
20200501-fur:
- fur
20200501-fy:
- fy
20200501-ga:
- ga
20200501-gag:
- gag
20200501-gan:
- gan
20200501-gd:
- gd
20200501-gl:
- gl
20200501-glk:
- glk
20200501-gn:
- gn
20200501-gom:
- gom
20200501-gor:
- gor
20200501-got:
- got
20200501-gu:
- gu
20200501-gv:
- gv
20200501-ha:
- ha
20200501-hak:
- hak
20200501-haw:
- haw
20200501-he:
- he
20200501-hi:
- hi
20200501-hif:
- hif
20200501-ho:
- ho
20200501-hr:
- hr
20200501-hsb:
- hsb
20200501-ht:
- ht
20200501-hu:
- hu
20200501-hy:
- hy
20200501-ia:
- ia
20200501-id:
- id
20200501-ie:
- ie
20200501-ig:
- ig
20200501-ii:
- ii
20200501-ik:
- ik
20200501-ilo:
- ilo
20200501-inh:
- inh
20200501-io:
- io
20200501-is:
- is
20200501-it:
- it
20200501-iu:
- iu
20200501-ja:
- ja
20200501-jam:
- jam
20200501-jbo:
- jbo
20200501-jv:
- jv
20200501-ka:
- ka
20200501-kaa:
- kaa
20200501-kab:
- kab
20200501-kbd:
- kbd
20200501-kbp:
- kbp
20200501-kg:
- kg
20200501-ki:
- ki
20200501-kj:
- kj
20200501-kk:
- kk
20200501-kl:
- kl
20200501-km:
- km
20200501-kn:
- kn
20200501-ko:
- ko
20200501-koi:
- koi
20200501-krc:
- krc
20200501-ks:
- ks
20200501-ksh:
- ksh
20200501-ku:
- ku
20200501-kv:
- kv
20200501-kw:
- kw
20200501-ky:
- ky
20200501-la:
- la
20200501-lad:
- lad
20200501-lb:
- lb
20200501-lbe:
- lbe
20200501-lez:
- lez
20200501-lfn:
- lfn
20200501-lg:
- lg
20200501-li:
- li
20200501-lij:
- lij
20200501-lmo:
- lmo
20200501-ln:
- ln
20200501-lo:
- lo
20200501-lrc:
- lrc
20200501-lt:
- lt
20200501-ltg:
- ltg
20200501-lv:
- lv
20200501-mai:
- mai
20200501-map-bms:
- unknown
20200501-mdf:
- mdf
20200501-mg:
- mg
20200501-mh:
- mh
20200501-mhr:
- mhr
20200501-mi:
- mi
20200501-min:
- min
20200501-mk:
- mk
20200501-ml:
- ml
20200501-mn:
- mn
20200501-mr:
- mr
20200501-mrj:
- mrj
20200501-ms:
- ms
20200501-mt:
- mt
20200501-mus:
- mus
20200501-mwl:
- mwl
20200501-my:
- my
20200501-myv:
- myv
20200501-mzn:
- mzn
20200501-na:
- na
20200501-nah:
- nah
20200501-nap:
- nap
20200501-nds:
- nds
20200501-nds-nl:
- nds-nl
20200501-ne:
- ne
20200501-new:
- new
20200501-ng:
- ng
20200501-nl:
- nl
20200501-nn:
- nn
20200501-no:
- 'no'
20200501-nov:
- nov
20200501-nrm:
- nrf
20200501-nso:
- nso
20200501-nv:
- nv
20200501-ny:
- ny
20200501-oc:
- oc
20200501-olo:
- olo
20200501-om:
- om
20200501-or:
- or
20200501-os:
- os
20200501-pa:
- pa
20200501-pag:
- pag
20200501-pam:
- pam
20200501-pap:
- pap
20200501-pcd:
- pcd
20200501-pdc:
- pdc
20200501-pfl:
- pfl
20200501-pi:
- pi
20200501-pih:
- pih
20200501-pl:
- pl
20200501-pms:
- pms
20200501-pnb:
- pnb
20200501-pnt:
- pnt
20200501-ps:
- ps
20200501-pt:
- pt
20200501-qu:
- qu
20200501-rm:
- rm
20200501-rmy:
- rmy
20200501-rn:
- rn
20200501-ro:
- ro
20200501-roa-rup:
- rup
20200501-roa-tara:
- unknown
20200501-ru:
- ru
20200501-rue:
- rue
20200501-rw:
- rw
20200501-sa:
- sa
20200501-sah:
- sah
20200501-sat:
- sat
20200501-sc:
- sc
20200501-scn:
- scn
20200501-sco:
- sco
20200501-sd:
- sd
20200501-se:
- se
20200501-sg:
- sg
20200501-sh:
- sh
20200501-si:
- si
20200501-simple:
- simple
20200501-sk:
- sk
20200501-sl:
- sl
20200501-sm:
- sm
20200501-sn:
- sn
20200501-so:
- so
20200501-sq:
- sq
20200501-sr:
- sr
20200501-srn:
- srn
20200501-ss:
- ss
20200501-st:
- st
20200501-stq:
- stq
20200501-su:
- su
20200501-sv:
- sv
20200501-sw:
- sw
20200501-szl:
- szl
20200501-ta:
- ta
20200501-tcy:
- tcy
20200501-te:
- te
20200501-tet:
- tdt
20200501-tg:
- tg
20200501-th:
- th
20200501-ti:
- ti
20200501-tk:
- tk
20200501-tl:
- tl
20200501-tn:
- tn
20200501-to:
- to
20200501-tpi:
- tpi
20200501-tr:
- tr
20200501-ts:
- ts
20200501-tt:
- tt
20200501-tum:
- tum
20200501-tw:
- tw
20200501-ty:
- ty
20200501-tyv:
- tyv
20200501-udm:
- udm
20200501-ug:
- ug
20200501-uk:
- uk
20200501-ur:
- ur
20200501-uz:
- uz
20200501-ve:
- ve
20200501-vec:
- vec
20200501-vep:
- vep
20200501-vi:
- vi
20200501-vls:
- vls
20200501-vo:
- vo
20200501-wa:
- wa
20200501-war:
- war
20200501-wo:
- wo
20200501-wuu:
- wuu
20200501-xal:
- xal
20200501-xh:
- xh
20200501-xmf:
- xmf
20200501-yi:
- yi
20200501-yo:
- yo
20200501-za:
- za
20200501-zea:
- zea
20200501-zh:
- zh
20200501-zh-classical:
- lzh
20200501-zh-min-nan:
- nan
20200501-zh-yue:
- yue
20200501-zu:
- zu
Dataset Card for "wikipedia"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://dumps.wikimedia.org
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 30739.25 MB
- Size of the generated dataset: 35376.35 MB
- Total amount of disk used: 66115.60 MB
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages. The datasets are built from the Wikipedia dump (https://dumps.wikimedia.org/) with one split per language. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.).
The articles are parsed using the mwparserfromhell tool.
To load this dataset you need to install Apache Beam and mwparserfromhell first:
pip install apache_beam mwparserfromhell
Then can load any subset of Wikipedia per language and per date this way:
from datasets import load_dataset
load_dataset("wikipedia", language="sw", date="20220120")
You can find the full list of languages and dates here.
Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
Languages
You can find the list of languages here.
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
Some subsets of Wikipedia have already been processed by Hugging face, as you can see below:
20200501.en
- Size of downloaded dataset files: 17396.28 MB
- Size of the generated dataset: 17481.07 MB
- Total amount of disk used: 34877.35 MB
An example looks as follows.
{
'title': 'Yangliuqing',
'text': 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin,
...
and traditional period furnishings and crafts.\n\nSee also \n\nList of township-level divisions of Tianjin\n\nReferences \n\n
http://arts.cultural-china.com/en/65Arts4795.html\n\nCategory:Towns in Tianjin'
}
20200501.de
- Size of downloaded dataset files: 5531.82 MB
- Size of the generated dataset: 7716.79 MB
- Total amount of disk used: 13248.61 MB
20200501.fr
- Size of downloaded dataset files: 4653.55 MB
- Size of the generated dataset: 6182.24 MB
- Total amount of disk used: 10835.79 MB
20200501.frr
- Size of downloaded dataset files: 9.05 MB
- Size of the generated dataset: 5.88 MB
- Total amount of disk used: 14.93 MB
20200501.it
- Size of downloaded dataset files: 2970.57 MB
- Size of the generated dataset: 3809.89 MB
- Total amount of disk used: 6780.46 MB
Data Fields
The data fields are the same among all splits and configurations:
title: astringfeature corresponding to the title of the articletext: astringfeature corresponding to the text content of the article
Data Splits
Here are the sizes for several configurations:
| name | train |
|---|---|
| 20200501.de | 3140341 |
| 20200501.en | 6078422 |
| 20200501.fr | 2210508 |
| 20200501.frr | 11803 |
| 20200501.it | 1931197 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@ONLINE {wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
Contributions
Thanks to @lewtun, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset.