Datasets:

tner
/

wikiann

	---
	language:
	- ace
	- bg
	- da
	- fur
	- ilo
	- lij
	- mzn
	- qu
	- su
	- vi
	- af
	- bh
	- de
	- fy
	- io
	- lmo
	- nap
	- rm
	- sv
	- vls
	- als
	- bn
	- diq
	- ga
	- is
	- ln
	- nds
	- ro
	- sw
	- vo
	- am
	- bo
	- dv
	- gan
	- it
	- lt
	- ne
	- ru
	- szl
	- wa
	- an
	- br
	- el
	- gd
	- ja
	- lv
	- nl
	- rw
	- ta
	- war
	- ang
	- bs
	- eml
	- gl
	- jbo
	- nn
	- sa
	- te
	- wuu
	- ar
	- ca
	- en
	- gn
	- jv
	- mg
	- no
	- sah
	- tg
	- xmf
	- arc
	- eo
	- gu
	- ka
	- mhr
	- nov
	- scn
	- th
	- yi
	- arz
	- cdo
	- es
	- hak
	- kk
	- mi
	- oc
	- sco
	- tk
	- yo
	- as
	- ce
	- et
	- he
	- km
	- min
	- or
	- sd
	- tl
	- zea
	- ast
	- ceb
	- eu
	- hi
	- kn
	- mk
	- os
	- sh
	- tr
	- ay
	- ckb
	- ext
	- hr
	- ko
	- ml
	- pa
	- si
	- tt
	- az
	- co
	- fa
	- hsb
	- ksh
	- mn
	- pdc
	- ug
	- ba
	- crh
	- fi
	- hu
	- ku
	- mr
	- pl
	- sk
	- uk
	- zh
	- bar
	- cs
	- hy
	- ky
	- ms
	- pms
	- sl
	- ur
	- csb
	- fo
	- ia
	- la
	- mt
	- pnb
	- so
	- uz
	- cv
	- fr
	- id
	- lb
	- mwl
	- ps
	- sq
	- vec
	- be
	- cy
	- frr
	- ig
	- li
	- my
	- pt
	- sr
	multilinguality:
	- multilingual
	size_categories:
	- 10K<100k
	task_categories:
	- token-classification
	task_ids:
	- named-entity-recognition
	pretty_name: WikiAnn
	---

	# Dataset Card for "tner/wikiann"

	## Dataset Description

	- Repository: [T-NER](https://github.com/asahi417/tner)
	- Paper: [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
	- Dataset: WikiAnn
	- Domain: Wikipedia
	- Number of Entity: 3


	### Dataset Summary
	WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
	- Entity Types: `LOC`, `ORG`, `PER`

	## Dataset Structure

	### Data Instances
	An example of `train` of `ja` looks as follows.

	```
	{
	'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'],
	'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6]
	}
	```

	### Label ID
	The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/btc/raw/main/dataset/label.json).
	```python
	{
	"B-LOC": 0,
	"B-ORG": 1,
	"B-PER": 2,
	"I-LOC": 3,
	"I-ORG": 4,
	"I-PER": 5,
	"O": 6
	}
	```

	### Data Splits

	\| language \| train \| validation \| test \|
	\|:-------------\|--------:\|-------------:\|-------:\|
	\| ace \| 100 \| 100 \| 100 \|
	\| bg \| 20000 \| 10000 \| 10000 \|
	\| da \| 20000 \| 10000 \| 10000 \|
	\| fur \| 100 \| 100 \| 100 \|
	\| ilo \| 100 \| 100 \| 100 \|
	\| lij \| 100 \| 100 \| 100 \|
	\| mzn \| 100 \| 100 \| 100 \|
	\| qu \| 100 \| 100 \| 100 \|
	\| su \| 100 \| 100 \| 100 \|
	\| vi \| 20000 \| 10000 \| 10000 \|
	\| af \| 5000 \| 1000 \| 1000 \|
	\| bh \| 100 \| 100 \| 100 \|
	\| de \| 20000 \| 10000 \| 10000 \|
	\| fy \| 1000 \| 1000 \| 1000 \|
	\| io \| 100 \| 100 \| 100 \|
	\| lmo \| 100 \| 100 \| 100 \|
	\| nap \| 100 \| 100 \| 100 \|
	\| rm \| 100 \| 100 \| 100 \|
	\| sv \| 20000 \| 10000 \| 10000 \|
	\| vls \| 100 \| 100 \| 100 \|
	\| als \| 100 \| 100 \| 100 \|
	\| bn \| 10000 \| 1000 \| 1000 \|
	\| diq \| 100 \| 100 \| 100 \|
	\| ga \| 1000 \| 1000 \| 1000 \|
	\| is \| 1000 \| 1000 \| 1000 \|
	\| ln \| 100 \| 100 \| 100 \|
	\| nds \| 100 \| 100 \| 100 \|
	\| ro \| 20000 \| 10000 \| 10000 \|
	\| sw \| 1000 \| 1000 \| 1000 \|
	\| vo \| 100 \| 100 \| 100 \|
	\| am \| 100 \| 100 \| 100 \|
	\| bo \| 100 \| 100 \| 100 \|
	\| dv \| 100 \| 100 \| 100 \|
	\| gan \| 100 \| 100 \| 100 \|
	\| it \| 20000 \| 10000 \| 10000 \|
	\| lt \| 10000 \| 10000 \| 10000 \|
	\| ne \| 100 \| 100 \| 100 \|
	\| ru \| 20000 \| 10000 \| 10000 \|
	\| szl \| 100 \| 100 \| 100 \|
	\| wa \| 100 \| 100 \| 100 \|
	\| an \| 1000 \| 1000 \| 1000 \|
	\| br \| 1000 \| 1000 \| 1000 \|
	\| el \| 20000 \| 10000 \| 10000 \|
	\| gd \| 100 \| 100 \| 100 \|
	\| ja \| 20000 \| 10000 \| 10000 \|
	\| lv \| 10000 \| 10000 \| 10000 \|
	\| nl \| 20000 \| 10000 \| 10000 \|
	\| rw \| 100 \| 100 \| 100 \|
	\| ta \| 15000 \| 1000 \| 1000 \|
	\| war \| 100 \| 100 \| 100 \|
	\| ang \| 100 \| 100 \| 100 \|
	\| bs \| 15000 \| 1000 \| 1000 \|
	\| eml \| 100 \| 100 \| 100 \|
	\| gl \| 15000 \| 10000 \| 10000 \|
	\| jbo \| 100 \| 100 \| 100 \|
	\| map-bms \| 100 \| 100 \| 100 \|
	\| nn \| 20000 \| 1000 \| 1000 \|
	\| sa \| 100 \| 100 \| 100 \|
	\| te \| 1000 \| 1000 \| 1000 \|
	\| wuu \| 100 \| 100 \| 100 \|
	\| ar \| 20000 \| 10000 \| 10000 \|
	\| ca \| 20000 \| 10000 \| 10000 \|
	\| en \| 20000 \| 10000 \| 10000 \|
	\| gn \| 100 \| 100 \| 100 \|
	\| jv \| 100 \| 100 \| 100 \|
	\| mg \| 100 \| 100 \| 100 \|
	\| no \| 20000 \| 10000 \| 10000 \|
	\| sah \| 100 \| 100 \| 100 \|
	\| tg \| 100 \| 100 \| 100 \|
	\| xmf \| 100 \| 100 \| 100 \|
	\| arc \| 100 \| 100 \| 100 \|
	\| cbk-zam \| 100 \| 100 \| 100 \|
	\| eo \| 15000 \| 10000 \| 10000 \|
	\| gu \| 100 \| 100 \| 100 \|
	\| ka \| 10000 \| 10000 \| 10000 \|
	\| mhr \| 100 \| 100 \| 100 \|
	\| nov \| 100 \| 100 \| 100 \|
	\| scn \| 100 \| 100 \| 100 \|
	\| th \| 20000 \| 10000 \| 10000 \|
	\| yi \| 100 \| 100 \| 100 \|
	\| arz \| 100 \| 100 \| 100 \|
	\| cdo \| 100 \| 100 \| 100 \|
	\| es \| 20000 \| 10000 \| 10000 \|
	\| hak \| 100 \| 100 \| 100 \|
	\| kk \| 1000 \| 1000 \| 1000 \|
	\| mi \| 100 \| 100 \| 100 \|
	\| oc \| 100 \| 100 \| 100 \|
	\| sco \| 100 \| 100 \| 100 \|
	\| tk \| 100 \| 100 \| 100 \|
	\| yo \| 100 \| 100 \| 100 \|
	\| as \| 100 \| 100 \| 100 \|
	\| ce \| 100 \| 100 \| 100 \|
	\| et \| 15000 \| 10000 \| 10000 \|
	\| he \| 20000 \| 10000 \| 10000 \|
	\| km \| 100 \| 100 \| 100 \|
	\| min \| 100 \| 100 \| 100 \|
	\| or \| 100 \| 100 \| 100 \|
	\| sd \| 100 \| 100 \| 100 \|
	\| tl \| 10000 \| 1000 \| 1000 \|
	\| zea \| 100 \| 100 \| 100 \|
	\| ast \| 1000 \| 1000 \| 1000 \|
	\| ceb \| 100 \| 100 \| 100 \|
	\| eu \| 10000 \| 10000 \| 10000 \|
	\| hi \| 5000 \| 1000 \| 1000 \|
	\| kn \| 100 \| 100 \| 100 \|
	\| mk \| 10000 \| 1000 \| 1000 \|
	\| os \| 100 \| 100 \| 100 \|
	\| sh \| 20000 \| 10000 \| 10000 \|
	\| tr \| 20000 \| 10000 \| 10000 \|
	\| zh-classical \| 100 \| 100 \| 100 \|
	\| ay \| 100 \| 100 \| 100 \|
	\| ckb \| 1000 \| 1000 \| 1000 \|
	\| ext \| 100 \| 100 \| 100 \|
	\| hr \| 20000 \| 10000 \| 10000 \|
	\| ko \| 20000 \| 10000 \| 10000 \|
	\| ml \| 10000 \| 1000 \| 1000 \|
	\| pa \| 100 \| 100 \| 100 \|
	\| si \| 100 \| 100 \| 100 \|
	\| tt \| 1000 \| 1000 \| 1000 \|
	\| zh-min-nan \| 100 \| 100 \| 100 \|
	\| az \| 10000 \| 1000 \| 1000 \|
	\| co \| 100 \| 100 \| 100 \|
	\| fa \| 20000 \| 10000 \| 10000 \|
	\| hsb \| 100 \| 100 \| 100 \|
	\| ksh \| 100 \| 100 \| 100 \|
	\| mn \| 100 \| 100 \| 100 \|
	\| pdc \| 100 \| 100 \| 100 \|
	\| simple \| 20000 \| 1000 \| 1000 \|
	\| ug \| 100 \| 100 \| 100 \|
	\| zh-yue \| 20000 \| 10000 \| 10000 \|
	\| ba \| 100 \| 100 \| 100 \|
	\| crh \| 100 \| 100 \| 100 \|
	\| fi \| 20000 \| 10000 \| 10000 \|
	\| hu \| 20000 \| 10000 \| 10000 \|
	\| ku \| 100 \| 100 \| 100 \|
	\| mr \| 5000 \| 1000 \| 1000 \|
	\| pl \| 20000 \| 10000 \| 10000 \|
	\| sk \| 20000 \| 10000 \| 10000 \|
	\| uk \| 20000 \| 10000 \| 10000 \|
	\| zh \| 20000 \| 10000 \| 10000 \|
	\| bar \| 100 \| 100 \| 100 \|
	\| cs \| 20000 \| 10000 \| 10000 \|
	\| fiu-vro \| 100 \| 100 \| 100 \|
	\| hy \| 15000 \| 1000 \| 1000 \|
	\| ky \| 100 \| 100 \| 100 \|
	\| ms \| 20000 \| 1000 \| 1000 \|
	\| pms \| 100 \| 100 \| 100 \|
	\| sl \| 15000 \| 10000 \| 10000 \|
	\| ur \| 20000 \| 1000 \| 1000 \|
	\| bat-smg \| 100 \| 100 \| 100 \|
	\| csb \| 100 \| 100 \| 100 \|
	\| fo \| 100 \| 100 \| 100 \|
	\| ia \| 100 \| 100 \| 100 \|
	\| la \| 5000 \| 1000 \| 1000 \|
	\| mt \| 100 \| 100 \| 100 \|
	\| pnb \| 100 \| 100 \| 100 \|
	\| so \| 100 \| 100 \| 100 \|
	\| uz \| 1000 \| 1000 \| 1000 \|
	\| be-x-old \| 5000 \| 1000 \| 1000 \|
	\| cv \| 100 \| 100 \| 100 \|
	\| fr \| 20000 \| 10000 \| 10000 \|
	\| id \| 20000 \| 10000 \| 10000 \|
	\| lb \| 5000 \| 1000 \| 1000 \|
	\| mwl \| 100 \| 100 \| 100 \|
	\| ps \| 100 \| 100 \| 100 \|
	\| sq \| 5000 \| 1000 \| 1000 \|
	\| vec \| 100 \| 100 \| 100 \|
	\| be \| 15000 \| 1000 \| 1000 \|
	\| cy \| 10000 \| 1000 \| 1000 \|
	\| frr \| 100 \| 100 \| 100 \|
	\| ig \| 100 \| 100 \| 100 \|
	\| li \| 100 \| 100 \| 100 \|
	\| my \| 100 \| 100 \| 100 \|
	\| pt \| 20000 \| 10000 \| 10000 \|
	\| sr \| 20000 \| 10000 \| 10000 \|
	\| vep \| 100 \| 100 \| 100 \|

	### Citation Information

	```
	@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://aclanthology.org/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.",
	}
	```