Update parquet files

README.md

@@ -1,651 +0,0 @@
----
-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: [email protected])
-
-### 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.

WiLI-2018 dataset/wili_2018-test.parquet

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WiLI-2018 dataset/wili_2018-train.parquet

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dataset_infos.json

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-{"WiLI-2018 dataset": {"description": "It is a benchmark dataset for language identification and contains 235000 paragraphs of 235 languages\n", "citation": "@dataset{thoma_martin_2018_841984,\n  author       = {Thoma, Martin},\n  title        = {{WiLI-2018 - Wikipedia Language Identification database}},\n  month        = jan,\n  year         = 2018,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.841984},\n  url          = {https://doi.org/10.5281/zenodo.841984}\n}\n", "homepage": "https://zenodo.org/record/841984", "license": "ODC Open Database License v1.0", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 235, "names": ["cdo", "glk", "jam", "lug", "san", "rue", "wol", "new", "mwl", "bre", "ara", "hye", "xmf", "ext", "cor", "yor", "div", "asm", "lat", "cym", "hif", "ace", "kbd", "tgk", "rus", "nso", "mya", "msa", "ava", "cbk", "urd", "deu", "swa", "pus", "bxr", "udm", "csb", "yid", "vro", "por", "pdc", "eng", "tha", "hat", "lmo", "pag", "jav", "chv", "nan", "sco", "kat", "bho", "bos", "kok", "oss", "mri", "fry", "cat", "azb", "kin", "hin", "sna", "dan", "egl", "mkd", "ron", "bul", "hrv", "som", "pam", "nav", "ksh", "nci", "khm", "sgs", "srn", "bar", "cos", "ckb", "pfl", "arz", "roa-tara", "fra", "mai", "zh-yue", "guj", "fin", "kir", "vol", "hau", "afr", "uig", "lao", "swe", "slv", "kor", "szl", "srp", "dty", "nrm", "dsb", "ind", "wln", "pnb", "ukr", "bpy", "vie", "tur", "aym", "lit", "zea", "pol", "est", "scn", "vls", "stq", "gag", "grn", "kaz", "ben", "pcd", "bjn", "krc", "amh", "diq", "ltz", "ita", "kab", "bel", "ang", "mhr", "che", "koi", "glv", "ido", "fao", "bak", "isl", "bcl", "tet", "jpn", "kur", "map-bms", "tyv", "olo", "arg", "ori", "lim", "tel", "lin", "roh", "sqi", "xho", "mlg", "fas", "hbs", "tam", "aze", "lad", "nob", "sin", "gla", "nap", "snd", "ast", "mal", "mdf", "tsn", "nds", "tgl", "nno", "sun", "lzh", "jbo", "crh", "pap", "oci", "hak", "uzb", "zho", "hsb", "sme", "mlt", "vep", "lez", "nld", "nds-nl", "mrj", "spa", "ceb", "ina", "heb", "hun", "que", "kaa", "mar", "vec", "frp", "ell", "sah", "eus", "ces", "slk", "chr", "lij", "nep", "srd", "ilo", "be-tarask", "bod", "orm", "war", "glg", "mon", "gle", "min", "ibo", "ile", "epo", "lav", "lrc", "als", "mzn", "rup", "fur", "tat", "myv", "pan", "ton", "kom", "wuu", "tcy", "tuk", "kan", "ltg"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "text-classification", "text_column": "sentence", "label_column": "label", "labels": ["ace", "afr", "als", "amh", "ang", "ara", "arg", "arz", "asm", "ast", "ava", "aym", "azb", "aze", "bak", "bar", "bcl", "be-tarask", "bel", "ben", "bho", "bjn", "bod", "bos", "bpy", "bre", "bul", "bxr", "cat", "cbk", "cdo", "ceb", "ces", "che", "chr", "chv", "ckb", "cor", "cos", "crh", "csb", "cym", "dan", "deu", "diq", "div", "dsb", "dty", "egl", "ell", "eng", "epo", "est", "eus", "ext", "fao", "fas", "fin", "fra", "frp", "fry", "fur", "gag", "gla", "gle", "glg", "glk", "glv", "grn", "guj", "hak", "hat", "hau", "hbs", "heb", "hif", "hin", "hrv", "hsb", "hun", "hye", "ibo", "ido", "ile", "ilo", "ina", "ind", "isl", "ita", "jam", "jav", "jbo", "jpn", "kaa", "kab", "kan", "kat", "kaz", "kbd", "khm", "kin", "kir", "koi", "kok", "kom", "kor", "krc", "ksh", "kur", "lad", "lao", "lat", "lav", "lez", "lij", "lim", "lin", "lit", "lmo", "lrc", "ltg", "ltz", "lug", "lzh", "mai", "mal", "map-bms", "mar", "mdf", "mhr", "min", "mkd", "mlg", "mlt", "mon", "mri", "mrj", "msa", "mwl", "mya", "myv", "mzn", "nan", "nap", "nav", "nci", "nds", "nds-nl", "nep", "new", "nld", "nno", "nob", "nrm", "nso", "oci", "olo", "ori", "orm", "oss", "pag", "pam", "pan", "pap", "pcd", "pdc", "pfl", "pnb", "pol", "por", "pus", "que", "roa-tara", "roh", "ron", "rue", "rup", "rus", "sah", "san", "scn", "sco", "sgs", "sin", "slk", "slv", "sme", "sna", "snd", "som", "spa", "sqi", "srd", "srn", "srp", "stq", "sun", "swa", "swe", "szl", "tam", "tat", "tcy", "tel", "tet", "tgk", "tgl", "tha", "ton", "tsn", "tuk", "tur", "tyv", "udm", "uig", "ukr", "urd", "uzb", "vec", "vep", "vie", "vls", "vol", "vro", "war", "wln", "wol", "wuu", "xho", "xmf", "yid", "yor", "zea", "zh-yue", "zho"]}], "builder_name": "wili_2018", "config_name": "WiLI-2018 dataset", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 65408201, "num_examples": 117500, "dataset_name": "wili_2018"}, "test": {"name": "test", "num_bytes": 66491260, "num_examples": 117500, "dataset_name": "wili_2018"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1ZzlIQvw1KNBG97QQCfdatvVrrbeLaM1u": {"num_bytes": 64716393, "checksum": "895b3892a1edba1702b0f2117b756204ccc177a1c285420234bdb5d717ad4100"}, "https://drive.google.com/uc?export=download&id=1Xx4kFc1Xdzz8AhDasxZ0cSa-a35EQSDZ": {"num_bytes": 65799958, "checksum": "663f32b6f7d8a26b83e251803d386f29dcd558762125f4f8289f2cef067d4ce8"}}, "download_size": 130516351, "post_processing_size": null, "dataset_size": 131899461, "size_in_bytes": 262415812}}

wili_2018.py

@@ -1,334 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""WiLI-2018, the Wikipedia language identification benchmark dataset"""
-
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@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}
-}
-"""
-
-_DESCRIPTION = """\
-It is a benchmark dataset for language identification and contains 235000 paragraphs of 235 languages
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://zenodo.org/record/841984"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "ODC Open Database License v1.0"
-
-
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_TRAIN_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=1ZzlIQvw1KNBG97QQCfdatvVrrbeLaM1u"
-_TEST_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=1Xx4kFc1Xdzz8AhDasxZ0cSa-a35EQSDZ"
-
-_CLASSES = [
-    "cdo",
-    "glk",
-    "jam",
-    "lug",
-    "san",
-    "rue",
-    "wol",
-    "new",
-    "mwl",
-    "bre",
-    "ara",
-    "hye",
-    "xmf",
-    "ext",
-    "cor",
-    "yor",
-    "div",
-    "asm",
-    "lat",
-    "cym",
-    "hif",
-    "ace",
-    "kbd",
-    "tgk",
-    "rus",
-    "nso",
-    "mya",
-    "msa",
-    "ava",
-    "cbk",
-    "urd",
-    "deu",
-    "swa",
-    "pus",
-    "bxr",
-    "udm",
-    "csb",
-    "yid",
-    "vro",
-    "por",
-    "pdc",
-    "eng",
-    "tha",
-    "hat",
-    "lmo",
-    "pag",
-    "jav",
-    "chv",
-    "nan",
-    "sco",
-    "kat",
-    "bho",
-    "bos",
-    "kok",
-    "oss",
-    "mri",
-    "fry",
-    "cat",
-    "azb",
-    "kin",
-    "hin",
-    "sna",
-    "dan",
-    "egl",
-    "mkd",
-    "ron",
-    "bul",
-    "hrv",
-    "som",
-    "pam",
-    "nav",
-    "ksh",
-    "nci",
-    "khm",
-    "sgs",
-    "srn",
-    "bar",
-    "cos",
-    "ckb",
-    "pfl",
-    "arz",
-    "roa-tara",
-    "fra",
-    "mai",
-    "zh-yue",
-    "guj",
-    "fin",
-    "kir",
-    "vol",
-    "hau",
-    "afr",
-    "uig",
-    "lao",
-    "swe",
-    "slv",
-    "kor",
-    "szl",
-    "srp",
-    "dty",
-    "nrm",
-    "dsb",
-    "ind",
-    "wln",
-    "pnb",
-    "ukr",
-    "bpy",
-    "vie",
-    "tur",
-    "aym",
-    "lit",
-    "zea",
-    "pol",
-    "est",
-    "scn",
-    "vls",
-    "stq",
-    "gag",
-    "grn",
-    "kaz",
-    "ben",
-    "pcd",
-    "bjn",
-    "krc",
-    "amh",
-    "diq",
-    "ltz",
-    "ita",
-    "kab",
-    "bel",
-    "ang",
-    "mhr",
-    "che",
-    "koi",
-    "glv",
-    "ido",
-    "fao",
-    "bak",
-    "isl",
-    "bcl",
-    "tet",
-    "jpn",
-    "kur",
-    "map-bms",
-    "tyv",
-    "olo",
-    "arg",
-    "ori",
-    "lim",
-    "tel",
-    "lin",
-    "roh",
-    "sqi",
-    "xho",
-    "mlg",
-    "fas",
-    "hbs",
-    "tam",
-    "aze",
-    "lad",
-    "nob",
-    "sin",
-    "gla",
-    "nap",
-    "snd",
-    "ast",
-    "mal",
-    "mdf",
-    "tsn",
-    "nds",
-    "tgl",
-    "nno",
-    "sun",
-    "lzh",
-    "jbo",
-    "crh",
-    "pap",
-    "oci",
-    "hak",
-    "uzb",
-    "zho",
-    "hsb",
-    "sme",
-    "mlt",
-    "vep",
-    "lez",
-    "nld",
-    "nds-nl",
-    "mrj",
-    "spa",
-    "ceb",
-    "ina",
-    "heb",
-    "hun",
-    "que",
-    "kaa",
-    "mar",
-    "vec",
-    "frp",
-    "ell",
-    "sah",
-    "eus",
-    "ces",
-    "slk",
-    "chr",
-    "lij",
-    "nep",
-    "srd",
-    "ilo",
-    "be-tarask",
-    "bod",
-    "orm",
-    "war",
-    "glg",
-    "mon",
-    "gle",
-    "min",
-    "ibo",
-    "ile",
-    "epo",
-    "lav",
-    "lrc",
-    "als",
-    "mzn",
-    "rup",
-    "fur",
-    "tat",
-    "myv",
-    "pan",
-    "ton",
-    "kom",
-    "wuu",
-    "tcy",
-    "tuk",
-    "kan",
-    "ltg",
-]
-
-
-class Wili_2018(datasets.GeneratorBasedBuilder):
-    """WiLI Language Identification Dataset"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="WiLI-2018 dataset",
-            version=VERSION,
-            description="Plain text of import of WiLI-2018",
-        )
-    ]
-
-    def _info(self):
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=datasets.Features(
-                {"sentence": datasets.Value("string"), "label": datasets.features.ClassLabel(names=_CLASSES)}
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="sentence", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-
-        with open(filepath, encoding="utf-8") as f:
-            for id_, line in enumerate(f):
-                text, label = line.rsplit(",", 1)
-                text = text.strip('"')
-                label = int(label.strip())
-                yield id_, {"sentence": text, "label": label - 1}