Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Size:
1M - 10M
ArXiv:
License:
metadata
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- ace
- af
- als
- am
- an
- ang
- ar
- arc
- arz
- as
- ast
- ay
- az
- ba
- bar
- be
- bg
- bh
- bn
- bo
- br
- bs
- ca
- cbk
- cdo
- ce
- ceb
- ckb
- co
- crh
- cs
- csb
- cv
- cy
- da
- de
- diq
- dv
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- eml
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- et
- eu
- ext
- fa
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- fo
- fr
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- vi
- vls
- vo
- vro
- wa
- war
- wuu
- xmf
- yi
- yo
- yue
- zea
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: wikiann-1
pretty_name: WikiANN
config_names:
- 'no'
- ace
- af
- als
- am
- an
- ang
- ar
- arc
- arz
- as
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- ay
- az
- ba
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- bn
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- ce
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- ckb
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- cs
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- pa
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- vec
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- vi
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- war
- wuu
- xmf
- yi
- yo
- zea
- zh
language_bcp47:
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- en-basiceng
- jv-x-bms
dataset_info:
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Dataset Card for WikiANN
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Massively Multilingual Transfer for NER
- Repository: Massively Multilingual Transfer for NER
- Paper: The original datasets come from the Cross-lingual name tagging and linking for 282 languages paper by Xiaoman Pan et al. (2018). This version corresponds to the balanced train, dev, and test splits of the original data from the Massively Multilingual Transfer for NER paper by Afshin Rahimi et al. (2019).
- Leaderboard:
- Point of Contact: Afshin Rahimi or Lewis Tunstall or Albert Villanova del Moral
Dataset Summary
WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus.
Supported Tasks and Leaderboards
named-entity-recognition: The dataset can be used to train a model for named entity recognition in many languages, or evaluate the zero-shot cross-lingual capabilities of multilingual models.
Languages
The dataset contains 176 languages, one in each of the configuration subsets. The corresponding BCP 47 language tags are:
| Language tag | |
|---|---|
| ace | ace |
| af | af |
| als | als |
| am | am |
| an | an |
| ang | ang |
| ar | ar |
| arc | arc |
| arz | arz |
| as | as |
| ast | ast |
| ay | ay |
| az | az |
| ba | ba |
| bar | bar |
| be | be |
| bg | bg |
| bh | bh |
| bn | bn |
| bo | bo |
| br | br |
| bs | bs |
| ca | ca |
| cdo | cdo |
| ce | ce |
| ceb | ceb |
| ckb | ckb |
| co | co |
| crh | crh |
| cs | cs |
| csb | csb |
| cv | cv |
| cy | cy |
| da | da |
| de | de |
| diq | diq |
| dv | dv |
| el | el |
| en | en |
| eo | eo |
| es | es |
| et | et |
| eu | eu |
| ext | ext |
| fa | fa |
| fi | fi |
| fo | fo |
| fr | fr |
| frr | frr |
| fur | fur |
| fy | fy |
| ga | ga |
| gan | gan |
| gd | gd |
| gl | gl |
| gn | gn |
| gu | gu |
| hak | hak |
| he | he |
| hi | hi |
| hr | hr |
| hsb | hsb |
| hu | hu |
| hy | hy |
| ia | ia |
| id | id |
| ig | ig |
| ilo | ilo |
| io | io |
| is | is |
| it | it |
| ja | ja |
| jbo | jbo |
| jv | jv |
| ka | ka |
| kk | kk |
| km | km |
| kn | kn |
| ko | ko |
| ksh | ksh |
| ku | ku |
| ky | ky |
| la | la |
| lb | lb |
| li | li |
| lij | lij |
| lmo | lmo |
| ln | ln |
| lt | lt |
| lv | lv |
| mg | mg |
| mhr | mhr |
| mi | mi |
| min | min |
| mk | mk |
| ml | ml |
| mn | mn |
| mr | mr |
| ms | ms |
| mt | mt |
| mwl | mwl |
| my | my |
| mzn | mzn |
| nap | nap |
| nds | nds |
| ne | ne |
| nl | nl |
| nn | nn |
| no | no |
| nov | nov |
| oc | oc |
| or | or |
| os | os |
| other-bat-smg | sgs |
| other-be-x-old | be-tarask |
| other-cbk-zam | cbk |
| other-eml | eml |
| other-fiu-vro | vro |
| other-map-bms | jv-x-bms |
| other-simple | en-basiceng |
| other-zh-classical | lzh |
| other-zh-min-nan | nan |
| other-zh-yue | yue |
| pa | pa |
| pdc | pdc |
| pl | pl |
| pms | pms |
| pnb | pnb |
| ps | ps |
| pt | pt |
| qu | qu |
| rm | rm |
| ro | ro |
| ru | ru |
| rw | rw |
| sa | sa |
| sah | sah |
| scn | scn |
| sco | sco |
| sd | sd |
| sh | sh |
| si | si |
| sk | sk |
| sl | sl |
| so | so |
| sq | sq |
| sr | sr |
| su | su |
| sv | sv |
| sw | sw |
| szl | szl |
| ta | ta |
| te | te |
| tg | tg |
| th | th |
| tk | tk |
| tl | tl |
| tr | tr |
| tt | tt |
| ug | ug |
| uk | uk |
| ur | ur |
| uz | uz |
| vec | vec |
| vep | vep |
| vi | vi |
| vls | vls |
| vo | vo |
| wa | wa |
| war | war |
| wuu | wuu |
| xmf | xmf |
| yi | yi |
| yo | yo |
| zea | zea |
| zh | zh |
Dataset Structure
Data Instances
This is an example in the "train" split of the "af" (Afrikaans language) configuration subset:
{
'tokens': ['Sy', 'ander', 'seun', ',', 'Swjatopolk', ',', 'was', 'die', 'resultaat', 'van', '’n', 'buite-egtelike', 'verhouding', '.'],
'ner_tags': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'langs': ['af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af'],
'spans': ['PER: Swjatopolk']
}
Data Fields
tokens: alistofstringfeatures.langs: alistofstringfeatures that correspond to the language of each token.ner_tags: alistof classification labels, with possible values includingO(0),B-PER(1),I-PER(2),B-ORG(3),I-ORG(4),B-LOC(5),I-LOC(6).spans: alistofstringfeatures, that is the list of named entities in the input text formatted as<TAG>: <mention>
Data Splits
For each configuration subset, the data is split into "train", "validation" and "test" sets, each containing the following number of examples:
| Train | Validation | Test | |
|---|---|---|---|
| ace | 100 | 100 | 100 |
| af | 5000 | 1000 | 1000 |
| als | 100 | 100 | 100 |
| am | 100 | 100 | 100 |
| an | 1000 | 1000 | 1000 |
| ang | 100 | 100 | 100 |
| ar | 20000 | 10000 | 10000 |
| arc | 100 | 100 | 100 |
| arz | 100 | 100 | 100 |
| as | 100 | 100 | 100 |
| ast | 1000 | 1000 | 1000 |
| ay | 100 | 100 | 100 |
| az | 10000 | 1000 | 1000 |
| ba | 100 | 100 | 100 |
| bar | 100 | 100 | 100 |
| bat-smg | 100 | 100 | 100 |
| be | 15000 | 1000 | 1000 |
| be-x-old | 5000 | 1000 | 1000 |
| bg | 20000 | 10000 | 10000 |
| bh | 100 | 100 | 100 |
| bn | 10000 | 1000 | 1000 |
| bo | 100 | 100 | 100 |
| br | 1000 | 1000 | 1000 |
| bs | 15000 | 1000 | 1000 |
| ca | 20000 | 10000 | 10000 |
| cbk-zam | 100 | 100 | 100 |
| cdo | 100 | 100 | 100 |
| ce | 100 | 100 | 100 |
| ceb | 100 | 100 | 100 |
| ckb | 1000 | 1000 | 1000 |
| co | 100 | 100 | 100 |
| crh | 100 | 100 | 100 |
| cs | 20000 | 10000 | 10000 |
| csb | 100 | 100 | 100 |
| cv | 100 | 100 | 100 |
| cy | 10000 | 1000 | 1000 |
| da | 20000 | 10000 | 10000 |
| de | 20000 | 10000 | 10000 |
| diq | 100 | 100 | 100 |
| dv | 100 | 100 | 100 |
| el | 20000 | 10000 | 10000 |
| eml | 100 | 100 | 100 |
| en | 20000 | 10000 | 10000 |
| eo | 15000 | 10000 | 10000 |
| es | 20000 | 10000 | 10000 |
| et | 15000 | 10000 | 10000 |
| eu | 10000 | 10000 | 10000 |
| ext | 100 | 100 | 100 |
| fa | 20000 | 10000 | 10000 |
| fi | 20000 | 10000 | 10000 |
| fiu-vro | 100 | 100 | 100 |
| fo | 100 | 100 | 100 |
| fr | 20000 | 10000 | 10000 |
| frr | 100 | 100 | 100 |
| fur | 100 | 100 | 100 |
| fy | 1000 | 1000 | 1000 |
| ga | 1000 | 1000 | 1000 |
| gan | 100 | 100 | 100 |
| gd | 100 | 100 | 100 |
| gl | 15000 | 10000 | 10000 |
| gn | 100 | 100 | 100 |
| gu | 100 | 100 | 100 |
| hak | 100 | 100 | 100 |
| he | 20000 | 10000 | 10000 |
| hi | 5000 | 1000 | 1000 |
| hr | 20000 | 10000 | 10000 |
| hsb | 100 | 100 | 100 |
| hu | 20000 | 10000 | 10000 |
| hy | 15000 | 1000 | 1000 |
| ia | 100 | 100 | 100 |
| id | 20000 | 10000 | 10000 |
| ig | 100 | 100 | 100 |
| ilo | 100 | 100 | 100 |
| io | 100 | 100 | 100 |
| is | 1000 | 1000 | 1000 |
| it | 20000 | 10000 | 10000 |
| ja | 20000 | 10000 | 10000 |
| jbo | 100 | 100 | 100 |
| jv | 100 | 100 | 100 |
| ka | 10000 | 10000 | 10000 |
| kk | 1000 | 1000 | 1000 |
| km | 100 | 100 | 100 |
| kn | 100 | 100 | 100 |
| ko | 20000 | 10000 | 10000 |
| ksh | 100 | 100 | 100 |
| ku | 100 | 100 | 100 |
| ky | 100 | 100 | 100 |
| la | 5000 | 1000 | 1000 |
| lb | 5000 | 1000 | 1000 |
| li | 100 | 100 | 100 |
| lij | 100 | 100 | 100 |
| lmo | 100 | 100 | 100 |
| ln | 100 | 100 | 100 |
| lt | 10000 | 10000 | 10000 |
| lv | 10000 | 10000 | 10000 |
| map-bms | 100 | 100 | 100 |
| mg | 100 | 100 | 100 |
| mhr | 100 | 100 | 100 |
| mi | 100 | 100 | 100 |
| min | 100 | 100 | 100 |
| mk | 10000 | 1000 | 1000 |
| ml | 10000 | 1000 | 1000 |
| mn | 100 | 100 | 100 |
| mr | 5000 | 1000 | 1000 |
| ms | 20000 | 1000 | 1000 |
| mt | 100 | 100 | 100 |
| mwl | 100 | 100 | 100 |
| my | 100 | 100 | 100 |
| mzn | 100 | 100 | 100 |
| nap | 100 | 100 | 100 |
| nds | 100 | 100 | 100 |
| ne | 100 | 100 | 100 |
| nl | 20000 | 10000 | 10000 |
| nn | 20000 | 1000 | 1000 |
| no | 20000 | 10000 | 10000 |
| nov | 100 | 100 | 100 |
| oc | 100 | 100 | 100 |
| or | 100 | 100 | 100 |
| os | 100 | 100 | 100 |
| pa | 100 | 100 | 100 |
| pdc | 100 | 100 | 100 |
| pl | 20000 | 10000 | 10000 |
| pms | 100 | 100 | 100 |
| pnb | 100 | 100 | 100 |
| ps | 100 | 100 | 100 |
| pt | 20000 | 10000 | 10000 |
| qu | 100 | 100 | 100 |
| rm | 100 | 100 | 100 |
| ro | 20000 | 10000 | 10000 |
| ru | 20000 | 10000 | 10000 |
| rw | 100 | 100 | 100 |
| sa | 100 | 100 | 100 |
| sah | 100 | 100 | 100 |
| scn | 100 | 100 | 100 |
| sco | 100 | 100 | 100 |
| sd | 100 | 100 | 100 |
| sh | 20000 | 10000 | 10000 |
| si | 100 | 100 | 100 |
| simple | 20000 | 1000 | 1000 |
| sk | 20000 | 10000 | 10000 |
| sl | 15000 | 10000 | 10000 |
| so | 100 | 100 | 100 |
| sq | 5000 | 1000 | 1000 |
| sr | 20000 | 10000 | 10000 |
| su | 100 | 100 | 100 |
| sv | 20000 | 10000 | 10000 |
| sw | 1000 | 1000 | 1000 |
| szl | 100 | 100 | 100 |
| ta | 15000 | 1000 | 1000 |
| te | 1000 | 1000 | 1000 |
| tg | 100 | 100 | 100 |
| th | 20000 | 10000 | 10000 |
| tk | 100 | 100 | 100 |
| tl | 10000 | 1000 | 1000 |
| tr | 20000 | 10000 | 10000 |
| tt | 1000 | 1000 | 1000 |
| ug | 100 | 100 | 100 |
| uk | 20000 | 10000 | 10000 |
| ur | 20000 | 1000 | 1000 |
| uz | 1000 | 1000 | 1000 |
| vec | 100 | 100 | 100 |
| vep | 100 | 100 | 100 |
| vi | 20000 | 10000 | 10000 |
| vls | 100 | 100 | 100 |
| vo | 100 | 100 | 100 |
| wa | 100 | 100 | 100 |
| war | 100 | 100 | 100 |
| wuu | 100 | 100 | 100 |
| xmf | 100 | 100 | 100 |
| yi | 100 | 100 | 100 |
| yo | 100 | 100 | 100 |
| zea | 100 | 100 | 100 |
| zh | 20000 | 10000 | 10000 |
| zh-classical | 100 | 100 | 100 |
| zh-min-nan | 100 | 100 | 100 |
| zh-yue | 20000 | 10000 | 10000 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
The original 282 datasets are associated with this article
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
while the 176 languages supported in this version are associated with the following article
@inproceedings{rahimi-etal-2019-massively,
title = "Massively Multilingual Transfer for {NER}",
author = "Rahimi, Afshin and
Li, Yuan and
Cohn, Trevor",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1015",
pages = "151--164",
}