task_categories:other annotations_creators:expert-generated annotations_creators:crowdsourced multilinguality:multilingual size_categories:100M<n<1B language:af language:ar language:az language:be language:bg language:bn language:br language:bs language:ca language:ch language:cs language:cv language:cy language:da language:de language:el language:en language:eo language:es language:et language:eu language:fa language:fi language:fo language:fr language:fy language:ga language:gd language:gl language:gn language:he language:hi language:hr language:hu language:hy language:ia language:id language:ie language:io language:is language:it language:ja language:jv language:ka language:kk language:km language:ko language:ku language:kw language:la language:lb language:lt language:lv language:mi language:mk language:ml language:mn language:mr language:ms language:mt language:my language:nb language:nl language:nn language:no language:oc language:pl language:pt language:qu language:rn language:ro language:ru language:sh language:sl language:sq language:sr language:sv language:sw language:ta language:te language:th language:tk language:tl language:tr language:tt language:ug language:uk language:ur language:uz language:vi language:vo language:yi language:zh language:ace language:acm language:acq language:aeb language:ajp language:ak language:als language:am language:apc language:ars language:ary language:arz language:as language:ast language:awa language:ayr language:azb language:azj language:ba language:bm language:ban language:bem language:bho language:bjn language:bo language:bug language:ceb language:cjk language:ckb language:crh language:dik language:dyu language:dz language:ee language:fj language:fon language:fur language:fuv language:gaz language:gu language:ht language:ha language:hne language:ig language:ilo language:kab language:kac language:kam language:kn language:ks language:kbp language:kea language:khk language:ki language:rw language:ky language:kmb language:kmr language:knc language:kg language:lo language:lij language:li language:ln language:lmo language:ltg language:lua language:lg language:luo language:lus language:lvs language:mag language:mai language:mar language:min language:mni language:mos language:npi language:nso language:nus language:ny language:ory language:pag language:pa language:pap language:pbt language:pes language:plt language:prs language:quy language:sg language:sa language:sat language:scn language:shn language:si language:sk language:sm language:sn language:sd language:so language:st language:sc language:ss language:su language:swh language:szl language:taq language:tg language:ti language:tpi language:tn language:ts language:tum language:tw language:tzm language:umb language:uzn language:vec language:war language:wo language:xh language:ydd language:yo language:yue language:zsm language:zu license:apache-2.0 region:us
A multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities.
@misc{muennighoff2022crosslingual,
      title={Crosslingual Generalization through Multitask Finetuning}, 
      author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
      year={2022},
      eprint={2211.01786},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
---
annotations_creators:
- expert-generated
- crowdsourced
language:
- af
- ar
- az
- be
- bg
- bn
- br
- bs
- ca
- ch
- cs
- cv
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fo
- fr
- fy
- ga
- gd
- gl
- gn
- he
- hi
- hr
- hu
- hy
- ia
- id
- ie
- io
- is
- it
- ja
- jv
- ka
- kk
- km
- ko
- ku
- kw
- la
- lb
- lt
- lv
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- nb
- nl
- nn
- 'no'
- oc
- pl
- pt
- qu
- rn
- ro
- ru
- sh
- sl
- sq
- sr
- sv
- sw
- ta
- te
- th
- tk
- tl
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- vo
- yi
- zh
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fur
- fuv
- gaz
- gd
- ga
- gl
- gn
- gu
- ht
- ha
- he
- hi
- hne
- hr
- hu
- hy
- ig
- ilo
- id
- is
- it
- jv
- ja
- kab
- kac
- kam
- kn
- ks
- ka
- kk
- kbp
- kea
- khk
- km
- ki
- rw
- ky
- kmb
- kmr
- knc
- kg
- ko
- lo
- lij
- li
- ln
- lt
- lmo
- ltg
- lb
- lua
- lg
- luo
- lus
- lvs
- mag
- mai
- ml
- mar
- min
- mk
- mt
- mni
- mos
- mi
- my
- nl
- nn
- nb
- npi
- nso
- nus
- ny
- oc
- ory
- pag
- pa
- pap
- pbt
- pes
- plt
- pl
- pt
- prs
- quy
- ro
- rn
- ru
- sg
- sa
- sat
- scn
- shn
- si
- sk
- sl
- sm
- sn
- sd
- so
- st
- es
- sc
- sr
- ss
- su
- sv
- swh
- szl
- ta
- taq
- tt
- te
- tg
- tl
- th
- ti
- tpi
- tn
- ts
- tk
- tum
- tr
- tw
- tzm
- ug
- uk
- umb
- ur
- uzn
- vec
- vi
- war
- wo
- xh
- ydd
- yo
- yue
- zh
- zsm
- zu
programming_language: 
- Java
- Python
- Jupyter-Notebook
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: xP3x
size_categories:
- 100M<n<1B
task_categories:
- other
---

Can be loaded via e.g.:

```python
from datasets import load_dataset
d = load_dataset("Muennighoff/xP3x-sample", "apps")
```


1,000 rows from random languages and splits of xP3x for each of the multilingual datasets represented in [xP3x](https://huggingface.co/datasets/Muennighoff/xP3x).