Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Size:
100K - 1M
ArXiv:
Tags:
language-identification
License:
File size: 10,483 Bytes
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---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ace
- af
- als
- am
- an
- ang
- ar
- arz
- as
- ast
- av
- ay
- az
- azb
- ba
- bar
- bcl
- be
- bg
- bho
- bjn
- bn
- bo
- bpy
- br
- bs
- bxr
- ca
- cbk
- cdo
- ce
- ceb
- chr
- ckb
- co
- crh
- cs
- csb
- cv
- cy
- da
- de
- diq
- dsb
- dty
- dv
- egl
- el
- en
- eo
- es
- et
- eu
- ext
- fa
- fi
- fo
- fr
- frp
- fur
- fy
- ga
- gag
- gd
- gl
- glk
- gn
- gu
- gv
- ha
- hak
- he
- hi
- hif
- hr
- hsb
- ht
- hu
- hy
- ia
- id
- ie
- ig
- ilo
- io
- is
- it
- ja
- jam
- jbo
- jv
- ka
- kaa
- kab
- kbd
- kk
- km
- kn
- ko
- koi
- kok
- krc
- ksh
- ku
- kv
- kw
- ky
- la
- lad
- lb
- lez
- lg
- li
- lij
- lmo
- ln
- lo
- lrc
- lt
- ltg
- lv
- lzh
- mai
- map
- mdf
- mg
- mhr
- mi
- min
- mk
- ml
- mn
- mr
- mrj
- ms
- mt
- mwl
- my
- myv
- mzn
- nan
- nap
- nb
- nci
- nds
- ne
- new
- nl
- nn
- nrm
- nso
- nv
- oc
- olo
- om
- or
- os
- pa
- pag
- pam
- pap
- pcd
- pdc
- pfl
- pl
- pnb
- ps
- pt
- qu
- rm
- ro
- roa
- ru
- rue
- rup
- rw
- sa
- sah
- sc
- scn
- sco
- sd
- sgs
- sh
- si
- sk
- sl
- sme
- sn
- so
- sq
- sr
- srn
- stq
- su
- sv
- sw
- szl
- ta
- tcy
- te
- tet
- tg
- th
- tk
- tl
- tn
- to
- tr
- tt
- tyv
- udm
- ug
- uk
- ur
- uz
- vec
- vep
- vi
- vls
- vo
- vro
- wa
- war
- wo
- wuu
- xh
- xmf
- yi
- yo
- zea
- zh
language_bcp47:
- be-tarask
- map-bms
- nds-nl
- roa-tara
- zh-yue
license:
- odbl
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: wili-2018
pretty_name: Wili2018
tags:
- language-identification
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
0: cdo
1: glk
2: jam
3: lug
4: san
5: rue
6: wol
7: new
8: mwl
9: bre
10: ara
11: hye
12: xmf
13: ext
14: cor
15: yor
16: div
17: asm
18: lat
19: cym
20: hif
21: ace
22: kbd
23: tgk
24: rus
25: nso
26: mya
27: msa
28: ava
29: cbk
30: urd
31: deu
32: swa
33: pus
34: bxr
35: udm
36: csb
37: yid
38: vro
39: por
40: pdc
41: eng
42: tha
43: hat
44: lmo
45: pag
46: jav
47: chv
48: nan
49: sco
50: kat
51: bho
52: bos
53: kok
54: oss
55: mri
56: fry
57: cat
58: azb
59: kin
60: hin
61: sna
62: dan
63: egl
64: mkd
65: ron
66: bul
67: hrv
68: som
69: pam
70: nav
71: ksh
72: nci
73: khm
74: sgs
75: srn
76: bar
77: cos
78: ckb
79: pfl
80: arz
81: roa-tara
82: fra
83: mai
84: zh-yue
85: guj
86: fin
87: kir
88: vol
89: hau
90: afr
91: uig
92: lao
93: swe
94: slv
95: kor
96: szl
97: srp
98: dty
99: nrm
100: dsb
101: ind
102: wln
103: pnb
104: ukr
105: bpy
106: vie
107: tur
108: aym
109: lit
110: zea
111: pol
112: est
113: scn
114: vls
115: stq
116: gag
117: grn
118: kaz
119: ben
120: pcd
121: bjn
122: krc
123: amh
124: diq
125: ltz
126: ita
127: kab
128: bel
129: ang
130: mhr
131: che
132: koi
133: glv
134: ido
135: fao
136: bak
137: isl
138: bcl
139: tet
140: jpn
141: kur
142: map-bms
143: tyv
144: olo
145: arg
146: ori
147: lim
148: tel
149: lin
150: roh
151: sqi
152: xho
153: mlg
154: fas
155: hbs
156: tam
157: aze
158: lad
159: nob
160: sin
161: gla
162: nap
163: snd
164: ast
165: mal
166: mdf
167: tsn
168: nds
169: tgl
170: nno
171: sun
172: lzh
173: jbo
174: crh
175: pap
176: oci
177: hak
178: uzb
179: zho
180: hsb
181: sme
182: mlt
183: vep
184: lez
185: nld
186: nds-nl
187: mrj
188: spa
189: ceb
190: ina
191: heb
192: hun
193: que
194: kaa
195: mar
196: vec
197: frp
198: ell
199: sah
200: eus
201: ces
202: slk
203: chr
204: lij
205: nep
206: srd
207: ilo
208: be-tarask
209: bod
210: orm
211: war
212: glg
213: mon
214: gle
215: min
216: ibo
217: ile
218: epo
219: lav
220: lrc
221: als
222: mzn
223: rup
224: fur
225: tat
226: myv
227: pan
228: ton
229: kom
230: wuu
231: tcy
232: tuk
233: kan
234: ltg
config_name: WiLI-2018 dataset
splits:
- name: train
num_bytes: 65408201
num_examples: 117500
- name: test
num_bytes: 66491260
num_examples: 117500
download_size: 130516351
dataset_size: 131899461
---
# Dataset Card for wili_2018
## 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:** https://zenodo.org/record/841984
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/pdf/1801.07779
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Thoma, Martin (Email: info@martin-thoma.de)
### Dataset Summary
WiLI-2018, the Wikipedia language identification benchmark dataset, contains 235000 paragraphs of 235 languages. The dataset is balanced and a train-test split is provided.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
235 Different Languages
## Dataset Structure
### Data Instances
```
{
'label': 207,
'sentence': 'Ti Turkia ket maysa a demokrata, sekular, unitario, batay-linteg a republika nga addaan ti taga-ugma a tinawtawid a kultura. Ti Turkia ket umadadu a naipatipon iti Laud babaen ti panagkameng kadagiti organisasion a kas ti Konsilo iti Europa, NATO, OECD, OSCE ken ti G-20 a dagiti kangrunaan nga ekonomia. Ti Turkia ket nangrugi a nakitulag ti napno a panagkameng iti Kappon ti Europa idi 2005, nga isu ket maysa idin a kumaduaan a kameng iti Europeano a Komunidad ti Ekonomia manipud idi 1963 ken nakadanon ti maysa a tulagan ti kappon ti aduana idi 1995. Ti Turkia ket nagtaraken iti asideg a kultural, politikal, ekonomiko ken industria a panakibiang iti Tengnga a Daya, dagiti Turko nga estado iti Tengnga nga Asia ken dagiti pagilian ti Aprika babaen ti panagkameng kadagiti organisasion a kas ti Turko a Konsilo, Nagsaupan nga Administrasion iti Turko nga Arte ken Kultura, Organisasion iti Islamiko a Panagtitinnulong ken ti Organisasion ti Ekonomiko a Panagtitinnulong.'
}
```
### Data Fields
[Needs More Information]
### Data Splits
175000 lines of text each for train and test data.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Thomas Martin
### Licensing Information
ODC Open Database License v1.0
### Citation Information
```
@dataset{thoma_martin_2018_841984,
author = {Thoma, Martin},
title = {{WiLI-2018 - Wikipedia Language Identification database}},
month = jan,
year = 2018,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.841984},
url = {https://doi.org/10.5281/zenodo.841984}
}
```
### Contributions
Thanks to [@Shubhambindal2017](https://github.com/Shubhambindal2017) for adding this dataset. |