Datasets:
Tasks:
Text Classification
Formats:
parquet
Sub-tasks:
language-identification
Size:
1M - 10M
License:
metadata
license: other
license_name: mixed
license_link: https://huggingface.co/datasets/baobabtech/millid-dataset#licensing
pretty_name: MillID, language identification for 1,000+ languages
task_categories:
- text-classification
task_ids:
- language-identification
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
language:
- aai
- aak
- aau
- aaz
- aba
- abk
- abn
- abq
- abs
- abt
- abx
- aby
- abz
- aca
- acd
- ace
- acf
- ach
- acm
- acn
- acr
- acu
- ada
- ade
- adh
- adi
- adj
- ady
- adz
- aeb
- aer
- aeu
- aey
- afr
- agd
- agg
- agm
- agn
- agr
- agt
- agu
- agx
- ahk
- aia
- aii
- ain
- ajg
- aji
- ajp
- akb
- ake
- akh
- akp
- aln
- alp
- alq
- als
- alt
- aly
- alz
- ame
- amf
- amh
- ami
- amk
- amm
- amn
- amp
- amr
- amu
- amx
- ang
- ann
- anp
- anv
- any
- aoi
- aoj
- aom
- aoz
- apb
- apc
- ape
- apn
- apr
- apt
- apu
- apw
- apy
- apz
- arb
- are
- arg
- arl
- arn
- arp
- arq
- ars
- ary
- arz
- asm
- aso
- ast
- ata
- atb
- atd
- atg
- ati
- atj
- att
- auc
- aui
- auy
- ava
- avk
- avt
- avu
- awa
- awb
- awx
- ayp
- ayr
- azb
- azg
- azj
- azz
- bak
- bam
- ban
- bao
- bar
- bas
- bba
- bbb
- bbc
- bbr
- bcc
- bch
- bci
- bcl
- bco
- bcw
- bdd
- bdh
- bdq
- bea
- bef
- bel
- bem
- ben
- beq
- bew
- bfd
- bgr
- bgs
- bgt
- bgz
- bhg
- bhl
- bho
- bhw
- bib
- big
- bim
- bin
- bis
- biu
- biv
- bjn
- bjp
- bjr
- bjv
- bkd
- bkq
- bku
- bla
- blh
- blk
- blt
- blw
- blz
- bmh
- bmk
- bmq
- bmr
- bmu
- bno
- bnp
- boa
- bod
- boj
- bon
- bos
- box
- bpr
- bps
- bpy
- bqc
- bqp
- bre
- brh
- bru
- brx
- bsn
- bsp
- bsq
- bss
- btd
- bth
- bts
- btx
- bud
- bug
- buk
- bul
- bum
- bus
- bvd
- bvr
- bvz
- bwd
- bwu
- bxh
- bxr
- byr
- byx
- bzd
- bzh
- bzi
- bzj
- caa
- cab
- cac
- caf
- cag
- cak
- cao
- cap
- caq
- car
- cas
- cat
- cav
- cax
- cbc
- cbi
- cbk
- cbr
- cbs
- cbt
- cbu
- cbv
- cce
- cco
- ccp
- ceb
- ceg
- cek
- ces
- cfm
- cgc
- cha
- chd
- che
- chf
- chk
- cho
- chq
- chr
- chu
- chv
- chw
- chz
- cjk
- cjp
- cjs
- cjv
- ckb
- ckm
- cko
- ckt
- clu
- cme
- cmn
- cmo
- cnh
- cni
- cnk
- cnr
- cnw
- coe
- cof
- cok
- con
- cop
- cor
- cos
- cot
- cpb
- cpc
- cpu
- cpy
- crh
- crj
- crk
- crl
- crm
- crn
- crs
- crx
- csb
- csw
- csy
- cta
- ctd
- cto
- ctp
- ctu
- cub
- cui
- cuk
- cul
- cut
- cux
- cwe
- cwt
- cya
- cym
- czt
- daa
- dad
- daf
- dag
- dah
- dak
- dan
- dar
- ddg
- ded
- des
- deu
- dga
- dgc
- dgi
- dgr
- dgz
- dhm
- dhv
- dig
- dik
- diq
- diu
- div
- dje
- djk
- djr
- dks
- dng
- dnj
- dnw
- dob
- doi
- dop
- dos
- drg
- dru
- dsb
- dtp
- dts
- dua
- duo
- dur
- dwr
- dww
- dyi
- dyo
- dyu
- dzo
- ebk
- efi
- ekk
- eko
- ell
- emi
- eml
- emp
- enb
- eng
- enl
- enm
- enq
- epo
- eri
- ese
- esi
- esk
- ess
- esu
- etr
- eus
- eve
- ewe
- ewo
- ext
- eza
- faa
- fai
- fal
- fan
- fao
- far
- fas
- fat
- ffm
- fij
- fil
- fin
- fit
- fkv
- fon
- for
- fra
- frd
- fro
- frp
- frr
- fry
- fub
- fue
- fuf
- fuh
- fur
- fuv
- gaa
- gag
- gah
- gai
- gam
- gaw
- gaz
- gbi
- gbo
- gcf
- gcr
- gde
- gdg
- gdn
- gdr
- geb
- gfk
- ghs
- gil
- gjn
- gkn
- gla
- gle
- glg
- glk
- glv
- gmh
- gmv
- gnb
- gnd
- gng
- gnn
- gnw
- gof
- gog
- goh
- gom
- gor
- gos
- got
- gqr
- grc
- grt
- gso
- gsw
- gub
- guc
- gug
- guh
- gui
- guj
- guk
- gul
- gum
- gun
- guo
- guq
- gur
- guw
- gux
- guz
- gvc
- gvf
- gvl
- gvn
- gwi
- gym
- gyr
- hac
- hae
- hag
- hak
- hat
- hau
- hav
- haw
- hay
- hbo
- hch
- heb
- heg
- heh
- her
- hif
- hig
- hil
- hin
- hix
- hla
- hmo
- hmr
- hne
- hnj
- hnn
- hns
- hop
- hot
- hra
- hrv
- hrx
- hsb
- hto
- hub
- hui
- hun
- hus
- huu
- huv
- hvn
- hwc
- hye
- hyw
- ian
- iba
- ibg
- ibo
- icr
- ido
- idu
- ifb
- ife
- ifk
- ifu
- ify
- ige
- ign
- ike
- ikk
- ikt
- ikw
- ile
- ilo
- imo
- ina
- inb
- ind
- inh
- ino
- iou
- ipi
- irk
- isd
- ish
- isl
- iso
- ita
- itv
- ium
- ivv
- iws
- ixl
- izr
- izz
- jac
- jae
- jam
- jav
- jbo
- jbu
- jic
- jiv
- jpn
- jra
- jvn
- kaa
- kab
- kac
- kal
- kam
- kan
- kao
- kaq
- kas
- kat
- kaz
- kbc
- kbd
- kbh
- kbm
- kbp
- kbq
- kbr
- kby
- kca
- kcg
- kck
- kdc
- kde
- kdh
- kdi
- kdl
- kea
- kek
- ken
- keo
- kew
- kez
- kff
- kgf
- kgk
- kgp
- kha
- khk
- khm
- khs
- khz
- kij
- kik
- kin
- kir
- kiu
- kix
- kjb
- kje
- kjh
- kjs
- kkc
- kki
- kkj
- kkl
- klt
- klv
- kmb
- kmg
- kmh
- kmk
- kmm
- kmo
- kmr
- kms
- kmu
- kmy
- knc
- kne
- knf
- kng
- knj
- knk
- knv
- koi
- koo
- kor
- kos
- kpe
- kpf
- kpg
- kpj
- kpr
- kpv
- kpw
- kpx
- kpz
- kqc
- kqe
- kqf
- kqn
- kqp
- kqw
- krc
- kri
- krj
- krl
- kru
- ksc
- ksd
- ksh
- ksp
- ksr
- kss
- ksw
- ktj
- kto
- ktu
- ktz
- kua
- kud
- kue
- kum
- kup
- kus
- kvg
- kvn
- kwd
- kwf
- kwi
- kwj
- kwn
- kwy
- kxc
- kxw
- kyc
- kyf
- kyg
- kyq
- kyu
- kyz
- kze
- kzj
- lac
- lad
- lai
- laj
- lam
- lao
- lat
- lbb
- lbe
- lbj
- lbk
- lcm
- lcp
- ldi
- lee
- lef
- leh
- leu
- lew
- lex
- lez
- lfn
- lgg
- lgl
- lhu
- lia
- lid
- lif
- lij
- lim
- lin
- lip
- lis
- lit
- liv
- ljp
- lki
- lld
- llg
- lmk
- lmo
- lmp
- lnd
- log
- loq
- loz
- lrc
- lsi
- lsm
- ltg
- ltz
- lua
- lub
- luc
- lue
- lug
- lun
- luo
- lus
- lvs
- lwg
- lwo
- lww
- lzh
- maa
- mad
- mag
- mah
- mai
- maj
- mak
- mal
- mam
- maq
- mar
- mas
- mau
- mav
- maz
- mbb
- mbc
- mbd
- mbf
- mbh
- mbi
- mbj
- mbl
- mbs
- mbt
- mca
- mcb
- mcd
- mcf
- mck
- mco
- mcp
- mcq
- mcu
- mdf
- mdy
- med
- mee
- mej
- mek
- men
- meq
- mer
- meu
- mfe
- mfi
- mfq
- mfy
- mfz
- mgh
- mgr
- mhi
- mhl
- mhr
- mhx
- mib
- mic
- mie
- mif
- mig
- mih
- mil
- mim
- min
- mio
- mip
- miq
- mir
- mit
- miy
- miz
- mjc
- mjw
- mkd
- mkl
- mkn
- mks
- mlh
- mlp
- mlt
- mlu
- mmn
- mmo
- mmx
- mna
- mnb
- mni
- mnk
- mns
- mnw
- mnx
- mny
- mog
- moh
- mop
- mor
- mos
- mox
- mph
- mpm
- mpp
- mps
- mpt
- mpx
- mqb
- mqj
- mqy
- mri
- mrj
- mrq
- mrw
- msb
- msk
- msy
- mta
- mti
- mto
- mtp
- muh
- mui
- mup
- mur
- mus
- mux
- muy
- mva
- mvn
- mvp
- mwc
- mwl
- mwn
- mwp
- mwq
- mwv
- mww
- mxb
- mxp
- mxq
- mxt
- mxv
- mya
- myk
- myu
- myv
- myw
- myx
- myy
- mza
- mzh
- mzk
- mzn
- mzw
- mzz
- naf
- nah
- nak
- nap
- naq
- nas
- nav
- nba
- nbe
- nbl
- nbq
- nbu
- nca
- nch
- ncj
- ncl
- ncq
- ncu
- ncx
- ndc
- nde
- ndh
- ndo
- nds
- neb
- new
- nfa
- nfr
- ngl
- ngp
- ngu
- nhe
- nhg
- nhi
- nho
- nhr
- nhu
- nhw
- nhx
- nhy
- nia
- nif
- nii
- nij
- nim
- nin
- niu
- niy
- njb
- njm
- njn
- njo
- njz
- nko
- nld
- nlg
- nmf
- nmo
- nmz
- nnb
- nno
- nnq
- nnw
- noa
- nob
- nod
- nog
- non
- nop
- not
- nou
- nov
- nph
- npi
- npl
- npy
- nqo
- nre
- nrf
- nrm
- nse
- nsm
- nsn
- nso
- nss
- nsu
- ntp
- ntr
- ntu
- nuj
- nus
- nuy
- nvm
- nwi
- nya
- nyf
- nyn
- nyo
- nyu
- nyy
- nzi
- nzm
- obo
- oci
- ogo
- ojb
- oke
- okv
- old
- olo
- omw
- ong
- ons
- opm
- orv
- ory
- oss
- ota
- ote
- otm
- oto
- otq
- ots
- otw
- ozm
- pab
- pad
- pag
- pah
- pam
- pan
- pao
- pap
- pau
- pbb
- pbt
- pcd
- pck
- pcm
- pdc
- pdt
- pem
- pfl
- pib
- pio
- pir
- pis
- pjt
- plg
- pls
- plt
- plu
- plw
- pma
- pmf
- pmq
- pms
- pnb
- pnt
- poe
- poh
- poi
- pol
- pon
- por
- pot
- pov
- poy
- ppk
- ppo
- prf
- prg
- pri
- ptp
- ptu
- pwg
- pwn
- pxm
- qub
- quc
- quf
- qug
- quh
- qup
- quw
- quy
- quz
- qva
- qvc
- qve
- qvh
- qvi
- qvm
- qvn
- qvs
- qvw
- qvz
- qwh
- qxh
- qxl
- qxn
- qxo
- qxr
- rad
- rai
- rap
- rar
- rav
- raw
- rcf
- rej
- rgu
- rhg
- ria
- rim
- rkb
- rki
- rmc
- rml
- rmn
- rmo
- rmq
- rmy
- rnd
- rng
- roh
- ron
- roo
- rop
- row
- rro
- rtm
- rub
- rue
- ruf
- rug
- run
- rup
- rus
- rwo
- sab
- sag
- sah
- san
- sas
- sat
- sba
- sbe
- sbl
- sby
- sck
- scn
- sco
- sda
- sdc
- sdh
- seh
- ses
- sey
- sfw
- sgb
- sgc
- sgh
- sgs
- sgw
- sgz
- shi
- shn
- shp
- sid
- sig
- sil
- sim
- sin
- sja
- skg
- skr
- slk
- sll
- slv
- sma
- sme
- smj
- smk
- sml
- smn
- smo
- sms
- sna
- snc
- snd
- snf
- snn
- snp
- snw
- sny
- som
- sop
- soq
- sot
- soy
- spa
- spl
- spm
- spp
- sps
- spy
- srd
- srm
- srn
- srp
- srq
- srr
- ssd
- ssg
- ssw
- ssx
- stp
- stq
- sua
- suc
- sue
- suk
- sun
- sur
- sus
- suz
- swc
- swe
- swg
- swh
- swp
- sxb
- sxn
- syb
- syc
- syl
- szb
- szl
- szy
- tab
- tac
- tah
- taj
- tam
- taq
- tar
- tat
- tav
- taw
- tay
- tbc
- tbg
- tbo
- tby
- tbz
- tca
- tcf
- tcs
- tcy
- tcz
- tdt
- ted
- tee
- tel
- tem
- teo
- ter
- tet
- tew
- tfr
- tgk
- tgo
- tgp
- tha
- thk
- thl
- tif
- tih
- tim
- tir
- tiv
- tiy
- tkl
- tkr
- tku
- tlf
- tlh
- tll
- tly
- tmd
- tna
- tnc
- tnk
- tnn
- tnp
- tob
- toc
- tod
- tog
- toh
- toi
- toj
- tok
- ton
- too
- top
- tos
- tpa
- tpi
- tpm
- tpp
- tpt
- tpz
- trc
- trn
- trp
- trq
- trv
- tsc
- tsg
- tsn
- tso
- tsw
- tsz
- ttc
- tte
- ttj
- tuc
- tue
- tuf
- tui
- tuk
- tum
- tuo
- tur
- tuv
- tvk
- tvl
- twi
- twu
- txq
- txu
- tyv
- tzh
- tzj
- tzm
- tzo
- ubr
- ubu
- udm
- udu
- uig
- ukr
- umb
- und
- upv
- ura
- urb
- urd
- urh
- urk
- urt
- usa
- usp
- uth
- uvh
- uvl
- uzn
- uzs
- vap
- var
- vec
- ven
- vep
- vid
- vie
- viv
- vls
- vmw
- vmy
- vol
- vro
- vun
- waj
- wal
- wap
- war
- way
- wba
- wbm
- wbp
- wed
- wer
- whk
- wib
- wim
- wiu
- wln
- wls
- wlv
- wmt
- wmw
- wnc
- wnu
- wol
- wos
- wrk
- wrs
- wsk
- wuu
- wuv
- xal
- xav
- xbi
- xbr
- xed
- xho
- xla
- xmf
- xmm
- xmv
- xnn
- xog
- xon
- xrb
- xsi
- xsm
- xsr
- xsu
- xtd
- xtm
- xtn
- xuo
- yaa
- yad
- yal
- yan
- yao
- yap
- yaq
- ybb
- yby
- ycn
- ydd
- yka
- yle
- yli
- yml
- yom
- yon
- yor
- yrb
- yre
- yrk
- yrl
- yss
- yua
- yue
- yuj
- yut
- yuw
- yva
- zaa
- zab
- zac
- zad
- zae
- zai
- zam
- zao
- zar
- zas
- zat
- zav
- zaw
- zdj
- zea
- zgh
- zia
- ziw
- zne
- zom
- zos
- zpa
- zpc
- zpg
- zpi
- zpl
- zpm
- zpo
- zpt
- zpu
- zpv
- zpz
- zsm
- zsr
- ztq
- zty
- zul
- zyb
- zyp
configs:
- config_name: default
data_files:
- split: train
path: data/train.parquet
- split: validation
path: data/validation.parquet
- split: test
path: data/test.parquet
- config_name: udhr
data_files:
- split: test
path: udhr/test.parquet
MillID: language identification for 1,000+ languages
MillID (MultiLingual Language IDentifier) is the dataset behind the MillID language identifiers (MillID tiny v1.5, MillID medium v1, MillID large v1.5; compared with GlotLID and fastText on the leaderboard). Passages of text labelled with their language, for training and evaluating a language identifier: 1,641 language-script labels (1,551 ISO 639-3 languages, 56 scripts), 2,390,082 passages, plus the Universal Declaration of Human Rights in 429 languages as a human-labelled test set.
The MillID models were trained and scored on a fuller build that also holds Save the Children's (23,104 passages) and Nal'ibali's (16,500 passages) text, which is not ours to redistribute. This dataset is that build without it: every other passage keeps its split and every label its id, so test here is the models' test split without those passages.
Built by Baobab Tech to identify the language of real documents, so most of the text comes from PDFs, cut the way a page is read: short lines, sentences and paragraphs. Code: baobab-tech/millid.
Configs
| Config | Split | Rows | What it is |
|---|---|---|---|
default |
train |
1,911,469 | |
default |
validation |
241,469 | |
default |
test |
237,144 | split by document from train, never by passage |
udhr |
test |
27,623 | UDHR-LID, labelled by the translators; 358 of its 429 labels are in default |
from datasets import load_dataset
ds = load_dataset("baobabtech/millid-dataset") # train / validation / test
udhr = load_dataset("baobabtech/millid-dataset", "udhr") # human-labelled test set
Fields
| Field | |
|---|---|
text |
the passage |
label |
ISO 639-3 + ISO 15924 script, e.g. eng_Latn, amh_Ethi, srp_Cyrl |
language, iso639_3, iso639_1, script |
the label spelled out |
size |
short (20 to 80 characters), sentence (up to 300) or paragraph (up to 1,000) |
source, doc_id, page, url, title |
where it came from |
license, attribution |
per row: filter to what your use allows, and credit accordingly |
label_quality |
publisher (the publisher's label), verified (GlotLID agrees), declared (see below) |
glotlid, glotlid_prob |
GlotLID's reading of the passage |
Sources
| Source | What | Passages | Labels from | Licence |
|---|---|---|---|---|
| HuggingFaceFW/finepdfs | PDF text from Common Crawl | 605,480 | GlotLID, per page | ODC-By 1.0 |
| HuggingFaceFW/fineweb-2 | Web text from Common Crawl | 1,648,616 | GlotLID | ODC-By 1.0 |
| cis-lmu/GlotStoryBook | Children's storybooks: African Storybook, Pratham Books, LIDA Stories, Little Cree Books | 91,800 | the publishers | per story: CC BY, CC BY-NC, CC BY-NC-SA, public domain |
| cis-lmu/GlotSparse | News sites in sparse languages | 15,000 | the sites | CC0 1.0 |
| CAWST resources | Water, sanitation and hygiene training material | 29,186 | CAWST | CC BY / CC BY-SA (CAWST open content) |
How it was made
- Passages. Text is NFKC-normalised, which folds the ligatures and Arabic presentation forms PDF text layers are full of; URLs, emails and dot leaders are removed; a passage must be mostly letters. Each page gives passages of three sizes, because short text is where language identification is hardest. At most 40 passages come from one PDF, 20 from one article and 10 from one web page. Exact duplicates are removed across every source.
- Labels. Publisher-labelled sources keep their label unless GlotLID is confident (≥ 0.8) a passage is in another major language, typically a copyright line or a credit (
publisher). FinePDFs and FineWeb-2 were labelled by GlotLID per document, so a passage from them is kept only when GlotLID's reading of the passage agrees (verified); for a language whose data GlotLID agrees with less than 70% of the time, the label being in its top three is enough (declared). - Text that is no language is removed, whatever its label: passages GlotLID is confident (≥ 0.8) are
zxx(no linguistic content). In these sources that is mostly a PDF whose font maps letters to the wrong code points, such as a Russian document whose text layer readsɉɪɟɫɥɟɞɨɜɚɧɢɟforПреследование. So is a document catalogued in the wrong language (a Yemen report catalogued as Estonian). - Coverage. A label needs at least 100 passages from two documents; 310 labels had less and are not included. Each source contributes at most a fixed number of passages per label, so the large languages do not dominate.
- Splits are by document, so no test passage shares a document with a training one. A label with fewer than five documents is split by page.
- UDHR is kept out of training. The Declaration is published everywhere and the crawls hold copies of it. 4,391 passages sharing a 30-character run of letters with a UDHR-LID sentence were removed, so
udhrtests recognition, not memory.
The smallest labels:
| Label | Language | Train | Validation | Test | Documents |
|---|---|---|---|---|---|
aeb_Latn |
Tunisian Arabic | 61 | 38 | 1 | 15 |
plw_Latn |
Brooke's Point Palawano | 77 | 18 | 8 | 14 |
sba_Latn |
Ngambay | 84 | 20 | 0 | 13 |
lis_Lisu |
Lisu | 92 | 7 | 6 | 15 |
ade_Latn |
Adele | 31 | 43 | 31 | 6 |
old_Latn |
Mochi | 82 | 13 | 11 | 31 |
nre_Latn |
Southern Rengma Naga | 54 | 10 | 42 | 8 |
rki_Mymr |
Rakhine | 89 | 20 | 0 | 13 |
labels.csv lists every label with its counts per split and source.
Limitations
- Labels from FinePDFs and FineWeb-2 are GlotLID's. A model trained on them learns GlotLID's labels, mistakes included, so
udhris the fair test. GlotLID was itself trained on UDHR and the Glot corpora, so its own score on them is optimistic. - Genre. For many low-resource languages the web is mostly religious text, and the storybooks are children's books. A model can learn the genre along with the language.
- Close languages (Bemba and Lamba, Zulu and Ndebele, Arabic varieties) are hard by nature; short passages of them are often genuinely ambiguous.
Licensing
There is no single licence. Every row carries its own in license, and its credit in attribution:
ODC-By 1.0: 2,254,096CC BY: 81,221CAWST open content (resources.cawst.org/cc): 20,680CC0 1.0: 15,000CC BY-NC: 6,277CC BY-SA 4.0: 4,602CC BY-NC-SA: 4,146CC BY 3.0: 1,949CC BY-SA: 1,787CC BY 4.0: 168Public Domain: 156
FinePDFs and FineWeb-2 are ODC-By 1.0, also subject to Common Crawl's terms of use. UDHR-LID is CC0. Rows marked NC are for non-commercial use only.
Citation
Please cite the sources: FinePDFs and FineWeb-2 (Hugging Face), GlotLID and GlotStoryBook / GlotSparse (Kargaran et al., CIS LMU Munich), and UDHR-LID.