Datasets:

unimelb-nlp
/

wikiann

	---
	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
	- el
	- eml
	- en
	- eo
	- es
	- et
	- eu
	- ext
	- fa
	- fi
	- fo
	- fr
	- frr
	- fur
	- fy
	- ga
	- gan
	- gd
	- gl
	- gn
	- gu
	- hak
	- he
	- hi
	- hr
	- hsb
	- hu
	- hy
	- ia
	- id
	- ig
	- ilo
	- io
	- is
	- it
	- ja
	- jbo
	- jv
	- ka
	- kk
	- km
	- kn
	- ko
	- ksh
	- ku
	- ky
	- la
	- lb
	- li
	- lij
	- lmo
	- ln
	- lt
	- lv
	- lzh
	- mg
	- mhr
	- mi
	- min
	- mk
	- ml
	- mn
	- mr
	- ms
	- mt
	- mwl
	- my
	- mzn
	- nan
	- nap
	- nds
	- ne
	- nl
	- nn
	- 'no'
	- nov
	- oc
	- or
	- os
	- pa
	- pdc
	- pl
	- pms
	- pnb
	- ps
	- pt
	- qu
	- rm
	- ro
	- ru
	- rw
	- sa
	- sah
	- scn
	- sco
	- sd
	- sgs
	- sh
	- si
	- sk
	- sl
	- so
	- sq
	- sr
	- su
	- sv
	- sw
	- szl
	- ta
	- te
	- tg
	- th
	- tk
	- tl
	- tr
	- tt
	- ug
	- uk
	- ur
	- uz
	- vec
	- vep
	- 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
	- ast
	- ay
	- az
	- ba
	- bar
	- be
	- bg
	- bh
	- bn
	- bo
	- br
	- bs
	- ca
	- cdo
	- ce
	- ceb
	- ckb
	- co
	- crh
	- cs
	- csb
	- cv
	- cy
	- da
	- de
	- diq
	- dv
	- el
	- en
	- eo
	- es
	- et
	- eu
	- ext
	- fa
	- fi
	- fo
	- fr
	- frr
	- fur
	- fy
	- ga
	- gan
	- gd
	- gl
	- gn
	- gu
	- hak
	- he
	- hi
	- hr
	- hsb
	- hu
	- hy
	- ia
	- id
	- ig
	- ilo
	- io
	- is
	- it
	- ja
	- jbo
	- jv
	- ka
	- kk
	- km
	- kn
	- ko
	- ksh
	- ku
	- ky
	- la
	- lb
	- li
	- lij
	- lmo
	- ln
	- lt
	- lv
	- mg
	- mhr
	- mi
	- min
	- mk
	- ml
	- mn
	- mr
	- ms
	- mt
	- mwl
	- my
	- mzn
	- nap
	- nds
	- ne
	- nl
	- nn
	- nov
	- oc
	- or
	- os
	- other-bat-smg
	- other-be-x-old
	- other-cbk-zam
	- other-eml
	- other-fiu-vro
	- other-map-bms
	- other-simple
	- other-zh-classical
	- other-zh-min-nan
	- other-zh-yue
	- pa
	- pdc
	- pl
	- pms
	- pnb
	- ps
	- pt
	- qu
	- rm
	- ro
	- ru
	- rw
	- sa
	- sah
	- scn
	- sco
	- sd
	- sh
	- si
	- sk
	- sl
	- so
	- sq
	- sr
	- su
	- sv
	- sw
	- szl
	- ta
	- te
	- tg
	- th
	- tk
	- tl
	- tr
	- tt
	- ug
	- uk
	- ur
	- uz
	- vec
	- vep
	- vi
	- vls
	- vo
	- wa
	- war
	- wuu
	- xmf
	- yi
	- yo
	- zea
	- zh
	language_bcp47:
	- be-tarask
	- en-basiceng
	- jv-x-bms
	dataset_info:
	- config_name: ace
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
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	'1': B-PER
	'2': I-PER
	'3': B-ORG
	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
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	- name: spans
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	dataset_size: 71352
	- config_name: af
	features:
	- name: tokens
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	- name: ner_tags
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	'2': I-PER
	'3': B-ORG
	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
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	- name: spans
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	- name: test
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	num_examples: 1000
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	dataset_size: 2116506
	- config_name: als
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
	names:
	'0': O
	'1': B-PER
	'2': I-PER
	'3': B-ORG
	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
	sequence: string
	- name: spans
	sequence: string
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	num_examples: 100
	- name: test
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	num_examples: 100
	- name: train
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	dataset_size: 105547
	- config_name: am
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
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	'0': O
	'1': B-PER
	'2': I-PER
	'3': B-ORG
	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
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	- name: spans
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	num_examples: 100
	- name: test
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	num_examples: 100
	- name: train
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	num_examples: 100
	download_size: 234008884
	dataset_size: 67454
	- config_name: an
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
	names:
	'0': O
	'1': B-PER
	'2': I-PER
	'3': B-ORG
	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
	sequence: string
	- name: spans
	sequence: string
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	- name: test
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	num_examples: 1000
	- name: train
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	num_examples: 1000
	download_size: 234008884
	dataset_size: 536568
	- config_name: ang
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
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	'0': O
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	'3': B-ORG
	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
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	- name: spans
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	- name: test
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	num_examples: 100
	- name: train
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	num_examples: 100
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	dataset_size: 69744
	- config_name: ar
	features:
	- name: tokens
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	- name: ner_tags
	sequence:
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	'5': B-LOC
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	- name: langs
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	- name: spans
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	- name: test
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	dataset_size: 9332021
	- config_name: arc
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
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	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
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	- name: spans
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	- name: test
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	num_examples: 100
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	dataset_size: 50903
	- config_name: arz
	features:
	- name: tokens
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	- name: ner_tags
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	'0': O
	'1': B-PER
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	'3': B-ORG
	'4': I-ORG
	'5': B-LOC
	'6': I-LOC
	- name: langs
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	- name: spans
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	- name: test
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	num_examples: 100
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	- config_name: as
	features:
	- name: tokens
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	'5': B-LOC
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	- name: langs
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	- name: spans
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	- name: test
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	num_examples: 100
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	- config_name: ast
	features:
	- name: tokens
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	'5': B-LOC
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	- name: spans
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	- name: test
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	- config_name: ay
	features:
	- name: tokens
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	'5': B-LOC
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	- name: langs
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	- name: spans
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	- name: test
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	num_examples: 100
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	dataset_size: 37659
	- config_name: az
	features:
	- name: tokens
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	- name: ner_tags
	sequence:
	class_label:
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	'5': B-LOC
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	- name: spans
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	- name: test
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	num_examples: 1000
	- name: train
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	dataset_size: 3185553
	- config_name: ba
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
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	'5': B-LOC
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	- name: spans
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	- config_name: bar
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
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	- name: test
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	dataset_size: 52009
	- config_name: bat-smg
	features:
	- name: tokens
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	- name: ner_tags
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	'0': O
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	'5': B-LOC
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	- name: test
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	- config_name: be
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
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	'4': I-ORG
	'5': B-LOC
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	- name: test
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	dataset_size: 4511468
	- config_name: be-x-old
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
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	'1': B-PER
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	'5': B-LOC
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	- name: langs
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	- name: test
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	- config_name: bg
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
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	'0': O
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	'5': B-LOC
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	- config_name: bh
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
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	- config_name: bn
	features:
	- name: tokens
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	- name: ner_tags
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	- config_name: bo
	features:
	- name: tokens
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	- name: test
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	dataset_size: 52210
	- config_name: br
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
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	names:
	'0': O
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	'5': B-LOC
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	- config_name: bs
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
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	'0': O
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	- config_name: ca
	features:
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
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	- config_name: zh-yue
	features:
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	- name: ner_tags
	sequence:
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	names:
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	'2': I-PER
	'3': B-ORG
	'4': I-ORG
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	'6': I-LOC
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	sequence: string
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	download_size: 234008884
	dataset_size: 19848816
	configs:
	- config_name: ace
	data_files:
	- split: validation
	path: ace/validation-*
	- split: test
	path: ace/test-*
	- split: train
	path: ace/train-*
	- config_name: af
	data_files:
	- split: validation
	path: af/validation-*
	- split: test
	path: af/test-*
	- split: train
	path: af/train-*
	- config_name: als
	data_files:
	- split: validation
	path: als/validation-*
	- split: test
	path: als/test-*
	- split: train
	path: als/train-*
	---

	# Dataset Card for WikiANN

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner)
	- Repository: [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner)
	- Paper: The original datasets come from the _Cross-lingual name tagging and linking for 282 languages_ [paper](https://www.aclweb.org/anthology/P17-1178/) 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](https://arxiv.org/abs/1902.00193) by Afshin Rahimi et al. (2019).
	- Leaderboard:
	- Point of Contact: [Afshin Rahimi](mailto:afshinrahimi@gmail.com) or [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com) or [Albert Villanova del Moral](albert@huggingface.co)

	### 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:
	```python
	{
	'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`: a `list` of `string` features.
	- `langs`: a `list` of `string` features that correspond to the language of each token.
	- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4), `B-LOC` (5), `I-LOC` (6).
	- `spans`: a `list` of `string` features, 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",
	}
	```


	### Contributions

	Thanks to [@lewtun](https://github.com/lewtun) and [@rabeehk](https://github.com/rabeehk) for adding this dataset.