African-African Parallel Sentences
Collection
Collection of sentence pairs or parallel corpus across African Languages for machine translation.
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703 items
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Updated
Dataset Viewer
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1.25
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480
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1.245025
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Allah woodi innde na?
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Mũrungu arĩ riĩtwa?
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1.244957
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14 Jooni, nde min, mawɗo mooɗon e moodibbo mooɗon, mi lootii koyɗe mooɗon, onon maa e haani lootindiron koyɗe.
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14Na rĩrĩ, kuona atĩ Niĩ, Mwathani na Mũrutani wanyu, nĩndamũthambia nyarĩrĩ-rĩ, o na inyuĩ nĩmwagĩrĩirwo mũthambanagie nyarĩrĩ mũndũ na ũrĩa ũngĩ.
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1.243344
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be contrary to, be diffident, be doubtful, be dubious,
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Tiga ngarari, njinũ na gĩcambio,
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1.242796
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23 Koo moye ganyuɗoyam wanyii Nyaako am maa.
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23Mũndũ ũrĩa ũũthũũrĩte, nĩathũũire Baba o nake.
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1.241946
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Allah yi'i ɗum wooɗi.
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Nake Ngai akĩona atĩ ũndũ ũcio warĩ mwega.
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1.238144
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50 Mi ɗaɓɓitantaa hoore am teddungal, ammaa e woodi goɗɗo ɗaɓɓitanayɗoyamngal, kanko woni gaɗayɗo kiita.
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50 No niĩ ndirecarĩria rũgooco;+ nĩ kũrĩ Ũrĩa ũcaragia na agatuanĩra.
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1.233769
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Hay laamɓe no hooloo wonde
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Arĩa metĩkĩtie
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1.233661
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4 To o wurtinii fuu baali maako, o ardotoɗi, ɗi tokkaymo, ngam e ɗi andi hononde maako.
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4Aarĩkia kuumia iria irĩ ciake ciothe-rĩ, acitongoragia, nacio ngʼondu ciake ikamũrũmĩrĩra, tondũ nĩciũĩ mũgambo wake.
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1.227906
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7 To on njokkootirii e am nden boliiɗe am e njokkootiri e mooɗon, toree koo ɗume ɗum ngiɗɗon, ɗum hokkay'onɗum.
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7Mũngĩikara thĩinĩ wakwa, nacio ciugo ciakwa ciikare thĩinĩ wanyu, hooyai o kĩrĩa gĩothe mũngĩenda, na nĩmũkũheo kĩo.
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1.226439
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43 Ngam ɗume on paamaayi ko mbiimi?
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43Mũraga gũtaũkĩrwo nĩ ũrĩa ndĩroiga nĩkĩ?
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1.223212
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Allah oo woni koolniiɗo.
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Cia kuire o iciĩtĩkĩtie Ngai.
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1.222164
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43 Ngam ɗume on paamataa ko mi wi'ata on?
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43Mũraga gũtaũkĩrwo nĩ ũrĩa ndĩroiga nĩkĩ?
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1.220622
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11 Ɓurɗo teddude hakkunde mooɗon laatoo golloowo mooɗon.
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11Ũrĩa mũnene thĩinĩ wanyu nĩatuĩkage ndungata yanyu.
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1.219962
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2 Njowo nder maɓɓe faataaɓe, njowo luttuɓe boo marɓe faamu.
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2Atano ao maarĩ akĩĩgu, na atano maarĩ oogĩ.
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1.218135
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be contrary to, be diffident, be doubtful, be dubious,
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He mata na ngaa atua oo runga,
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1.215083
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2 Yeesu e tokkuɓemo maa kumpitaama humto teegal ngal.
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2Naake Jesũ hamwe na arutwo aake nĩmeetĩirwo ũhiki ũcio.
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1.214091
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26 E nden ɗon nyalnde on toroto nder inde am.
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26Mũthenya ũcio nĩmũkahooyaga thĩinĩ wa rĩĩtwa rĩakwa.
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1.211462
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Yimɓe ngari haa maako diga kootoy fuu.
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No andũ no mookaga kũrĩ we moimĩte kũndũ guothe.
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1.210589
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11 Ɓurɗo teddude hakkunde mooɗon yo laato gollanoowo heddiiɓe ɓee.
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11Ũrĩa mũnene thĩinĩ wanyu nĩatuĩkage ndungata yanyu.
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1.210574
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7 To on ɗon tabiti nder kawtal bee am, bolle am boo ɗon tabiti nder mooɗon, nden kam to on torake ko on ngiɗi fuu, on keɓan ɗum.
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7Mũngĩikara thĩinĩ wakwa, nacio ciugo ciakwa ciikare thĩinĩ wanyu, hooyai o kĩrĩa gĩothe mũngĩenda, na nĩmũkũheo kĩo.
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1.210127
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12 "E mi woodi fii ɗuɗɗum ɗum ngiɗumi wiiki'on, ammaa jooni ɗum teddanay'on.
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12"Ndĩ na maũndũ mangĩ maingĩ ma kũmwĩra, makĩria ya marĩa mũngĩhota kũũmĩrĩria rĩu.
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1.2081
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32 Ngam ɗume?
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32Maagire kũgĩa naguo nĩkĩ?
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1.207571
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22 - Gite ngoni lampal ɓanndu.
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22"Riitho nĩrĩo tawa wa mwĩrĩ.
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1.206348
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Ɗume Yeesu ɗon waɗa jotta?
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Jesu mahinda-inĩ maya areka atĩa?
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1.204829
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37 Koo nde Yeesu waɗii ko hayɗini ɗuɗɗum yeeso maɓɓe, fuu e non ɓe koolaakimo.
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o na ekĩte magegania make, matiamwĩtĩkirie.
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1.203963
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47 Ammaa nde on njaɓaayi ko o windi, no njaɓirton ko mbiimi?"
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47No kuona atĩ mũtiĩtĩkĩtie ũrĩa aandĩkire-rĩ, mũngĩgĩtĩkia atĩa ũrĩa njugaga?"
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1.203961
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45 Ammaa ngam mi wi'i goonga, on koolaakiyam.
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45No tondũ njaragia ũhoro wa ma-rĩ, mũtinjĩtĩkagia.
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1.203726
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Yeesu wi'i ɓe: "Nden kam min boo mi wi'ataa on bee baawɗe ɗeye mi huuwrata kuuje ɗe."
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8Nake Jesũ akĩmeera atĩrĩ, "O na niĩ ndikũmwĩra njĩkaga maũndũ maya na ũhoti ũrĩkũ."
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1.203637
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12 Nanee belɗum, mbelmbeltee, ngam barka mooɗon ɗum ɗuɗɗum e dow, ngam non ɓe ngaɗannoo annabo'en arande'en.
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12Kenai na mũcanjamũke, tondũ kĩheo kĩanyu nĩ kĩnene igũrũ, nĩgũkorwo ũguo noguo maanyariirire anabii arĩa maarĩ kuo mbere yanyu.
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1.20353
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42 Ammaa e mi andi on ngiɗaa Allah nder ɓerɗe mooɗon.
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Ngamenya atĩ inyuĩ mũtirĩ na wendo wa Ngai ngoro-inĩ cianyu.
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1.202652
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11 Woɗɓe mooɗon laatino bana non.
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11Na ũguo nĩguo amwe anyu mwatariĩ.
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1.200841
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Yimɓe fuu kayɗini.
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Naguo ũndũ ũcio ũkĩgegia andũ othe.
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1.200826
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36 Ngam non to Ɓiɗɗo on rimɗinii'on, on keɓii rimɗineeki ki goonga.
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36Nĩ ũndũ ũcio Mũrũ wa Ngai angĩkamũkũũra-rĩ, nĩmũgakũũrwo kũna.
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1.199636
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Ki yani ko e hitaande 1980.
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ngaahika ndeenda 1980
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1.198442
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9 O woodi ɓiɓɓe rewɓe nayo ɓe te'aaka, ɓe ɗon ngaajo wolde Allah.
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9Nake nĩarĩ na airĩtu aake ana matarĩ ahiku, nao nĩmarathaga mohoro.
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1.197867
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Allah tagri koo ɗume fuu bee maare, walaa ko taga bilaa maare.
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Indo ciothe ciombirwo nĩ Ngai agereire harĩ we, na gũtirĩ o na kĩmwe kĩombirwo we atarĩ ho.
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1.197652
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36 Ngam non to Ɓiɗɗo on rimɗinii'on, on keɓii rimɗineeki ki goonga.
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36 Kwoguo Mũriũ angĩmũkũũra ũkombo-inĩ, mũngĩkorũo mũrĩ akũũre kũna.
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1.195999
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35 Yahaya e nandi e fitilaaru kuɓɓayndu hokka jayngol, nden ngam carel seɗɗa on cuɓii nanki belɗum jayngol.
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35Johana aarĩ tawa ũrĩa wakanire na ũkĩmũrĩka, na inyuĩ mũgĩthuura gũkenera ũtheri wake kwa ihinda.
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1.195655
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13 Woodi kuuje feere ɗuuɗɗe ɗe mi anndinta on, ammaa mi yiɗaa windugo ɗum dow ɗereewol bee bindirgol.
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13Ndĩ na maũndũ maingĩ ma gũkwandĩkĩra, no ndikwenda gũkwandĩkĩra na karamu na rangi.
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1.19433
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53 Caggal mum, gooto fuu hooti suudu mum.
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53 (O mũndũ akĩinũka gwake mũciĩ.
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1.193823
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40 Nde o haalata ɗum ndee, o holli ɓe juuɗe makko e koyɗe makko.
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40Aarĩkia kuuga ũguo, akĩmoonia moko make na nyarĩrĩ ciake.
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1.193755
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Ndaaren misaaluuji seɗɗa.
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Ta wĩcirie ngerekano ici.
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1.193654
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30 Jooni e min andi a andi koo ɗume, naa' naa goɗɗo ƴamiima ƴamɗe.
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3016:30 1Ath 17:24Rĩu nĩtwamenya atĩ wee nĩũũĩ maũndũ mothe, na atĩ o na ndũbataire mũndũ o na ũrĩkũ akũũrie ciũria.
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1.193625
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7 Ɓaawo saa'aaji tati debbo maako wari, ammaa o anndaa ko waɗi goriiko.
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7Thuutha wa ta mathaa matatũ-rĩ, mũtumia wake agĩtoonya atekũmenya ũrĩa gwekĩkĩte.
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1.191739
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12 Min njotti wuro Sirakus, min paɓɓi ton balɗe tati.
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12Twakinya Sirakusi tũgĩikara kuo ihinda rĩa thikũ ithatũ.
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1.190662
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be contrary to, be diffident, be doubtful, be dubious,
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Nĩ ũndũ wa irumi na maheeni marĩa maaragia,
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1.190257
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How to be wise, be good, be obedient, and be safe,
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Athuthaanae perumpaadu, Thananaanana, thananaanana,
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1.189764
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33 On lili yimɓe to Yahaya, kanko boo o hokki'on ceyda goonga dow am.
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33"Inyuĩ nĩmwatũmanire kũrĩ Johana, nake nĩaheanire ũira wa ma.
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1.188647
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47 Ammaa Suleymaanu on nyiɓani mo ndu.
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47No Solomoni nĩwe wamwakĩire nyũmba.
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1.188375
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32 Daga duuniyaaru tagaa walaa meeɗuɗo nanuki goɗɗo wumtinii neɗɗo danyaaɗo e wumi.
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32Gũtirĩ mũndũ ũrĩ waigua ũhoro wa mũndũ ũhingũrĩtwo maitho aciarĩtwo arĩ mũtumumu.
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1.18751
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10 Laamu maaɗa wara, ko ngiɗɗaa waɗee nder duuniyaaru, no ɗum waɗirtee e dow.
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Ithe witu waiguru ritwa riaku riamurwo', uthamaki waku niuki gwikwo guku thi otauria gwikagwo kuu iguru.
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1.187219
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Ko haɗi'on nanki ko mbiimi woni onon naa' ɗum ɓe Allah."
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Kĩrĩa kĩgiragia mũmũigue nĩ tondũ mũtirĩ a Ngai."
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1.186911
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O danyay ɓiɗɗo gorko nden a noddaymo Yeesu, ngam o hisnay yimɓe maako daga hakke maɓɓe."
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21#Luk 1.31.Nake nĩagaciara kahĩĩ, nako nĩũgagatua JESU, nĩ ũndũ nĩwe ũkahonokia andũ ake mehia-inĩ mao.
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1.184834
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25 Naa' naa goɗɗo hokkiimo ceyda dow yimɓe, ngam kanko e hoore maako e mo andi ko woni nder ɓernde koo moye.
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252:25 Joh 6:61, 64Ndaabataragio nĩ ũira wa mũndũ, ũkoniĩ mũndũ ũrĩa ũngĩ, nĩgũkorwo nĩamenyaga kĩrĩa kĩrĩ thĩinĩ wa mũndũ.
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1.184782
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be contrary to, be diffident, be doubtful, be dubious,
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Marĩ mahsĩa, Marĩ da'raá, Marĩ dua'a (Nosso conhecimento, Nosso trabalho,
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1.184677
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20Onsay kamɓe Yahuudiyankeeɓe ɓen ɓe wi'i: "E nder duuɓi cappanɗe nay e jeegoo ndee juulirde no darneede, an non a immintinay nde e nder balɗe tati?"
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20Nao makĩmũũria atĩrĩ, "Hekaarũ ĩno yakirwo na mĩaka mĩrongo ĩna na ĩtandatũ nĩ yo ũroiga atĩ nĩũkũmĩaka na mĩthenya ĩtatũ?"
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1.184641
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Ndaaree ko heyɗi!
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Rora ũrĩ igũrũ wone!
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1.184505
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i wanna be rich too.
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O na ndĩ o mũchangi.
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1.184229
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25 Naa' naa goɗɗo hokkiimo ceyda dow yimɓe, ngam kanko e hoore maako e mo andi ko woni nder ɓernde koo moye.
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25Ndabataragio nĩkũheeo ũhoro wa mũndũ nĩ mũndũ ũngĩ tondũ we mwene nĩoĩ ngoro cia andũ.
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1.1842
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O wi'i, "Ee, Joomiraawo, a andi e mi yiɗumaa."
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318:3 1Sam 1:18Akiuga atĩrĩ, "Mwathi wakwa, angĩkorwo nĩndetĩkĩrĩka maitho-inĩ maku, ndũkahĩtũke ndungata yaku.
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1.184023
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Muusa wi'i ɓe: "Nane le, onon tuurtooɓe!
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Moisés yoikĩ kirika keneni keskara ato Akka ẽ omiskõimakĩ finata ẽ nĩchi
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1.184011
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4 Seyo fuu ɓuranaa kam ko nan-mi ɓiɓɓe am ana njokki goonga oo koo.
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44 1Joh 2:1Gũtirĩ ũndũ ũngĩ ũngenagia gũkĩra kũigua atĩ ciana ciakwa no ithiiaga irũmĩrĩire njĩra ĩrĩa ya ũhoro ũrĩa wa ma.
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1.183686
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4 Min mbindani on ɗum ngam haa seyo heewa ɓerɗe meeɗen.
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4 Tũramwandĩkĩra ũndũ ũyũ nĩ geetha gĩkeno giitũ gĩtuĩke gikinyanĩru.
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1.183263
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19 Ndelle, winndu ko nji'uɗaa ɗuum, ko waɗata joonin ɗuum e ko waroyta yeeso.
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19"Nĩ ũndũ ũcio, andĩka maũndũ marĩa wona, marĩa marĩ kuo rĩu na marĩa magooka thuutha-inĩ.
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1.182823
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13 Amma jooni, no kawtuɗon e Yeesu Almasiihu, onon woɗɗinooɓe, on ɓattinaama diga e ƴiiƴam makko deƴƴitiiɗam.
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13No rĩu, thĩinĩ wa Kristũ Jesũ, inyuĩ arĩa hĩndĩ ĩmwe mwarĩ kũraya, rĩu nĩmũreharehetwo hakuhĩ nĩ ũndũ wa thakame ya Kristũ.
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1.182524
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Ɓe fuu ɓe kayɗini.
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Naguo ũndũ ũcio ũkĩgegia andũ othe.
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1.18227
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24 On wumɓe ɗowooɓe yimɓe!
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24Inyuĩ atongoria aya atumumu!
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1.181885
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There'll be some weet een aw'll be beawnd;
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nakuo kũu kũriku gũgaathiũrũrũkĩria;
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1.18184
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Hokku min nyaamdu deydey
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Ũtũhe ũmũthĩ irio citũ cia gũtũigana- Give us this day sufficient food (give us this day our daily bread)
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1.18042
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19 Onon worɓe, jiɗee rewɓe mooɗon, to on monnanee ɓe.
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19Na inyuĩ athuuri-rĩ, endagai atumia anyu na mũtikamakũũmage.
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1.180355
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15 Hande nun an bo, aa woodi tokkuɓe ekkitinol Nikolayiiɓe.
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15O na ningĩ no ũrĩ na andũ amwe marũmĩtie ũrutani wa andũ arĩa metagwo Anikolai.
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1.179329
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14 Ndelle, miin Joomii on, miin Moodibbo, mi lootii koyɗe mon, tilay onon du lootunduron koyɗe.
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14Na rĩrĩ, kuona atĩ Niĩ, Mwathani na Mũrutani wanyu, nĩndamũthambia nyarĩrĩ-rĩ, o na inyuĩ nĩmwagĩrĩirwo mũthambanagie nyarĩrĩ mũndũ na ũrĩa ũngĩ.
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1.179029
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be contrary to, be diffident, be doubtful, be dubious,
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Njĩra ĩrĩa ũgeraga nĩ ũthingu na ũhoro ũrĩa wa ma
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1.178626
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12 Nanee belɗum, ngamee, sabu laada ɗuuɗka e reenti on toon dow, gam noon ɓe torrirnoo annabo'en aartuɓe on.
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12Kenai na mũcanjamũke, tondũ kĩheo kĩanyu nĩ kĩnene igũrũ, nĩgũkorwo ũguo noguo maanyariirire anabii arĩa maarĩ kuo mbere yanyu.
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1.178621
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4 To maako yonki ƴiwoyi, nden yonki kin woni jayngol yimɓe.
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4Thĩinĩ wake nĩkwarĩ muoyo, naguo muoyo ũcio nĩguo warĩ ũtheri wa andũ.
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1.17804
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8 To on nasti wuro kooŋgoye, ɓe njaɓɓake on, nyaame ko ɓe ngaddani on.
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8"Mũngĩgatoonya itũũra mũnyiitwo ũgeni-rĩ, rĩĩagai o kĩrĩa mũngĩheo.
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1.177567
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Wo onon jaati! 20 Si goonga, onon ngoni teddeengal amin e seyo amin.
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Githĩ ti inyuĩ? 20Ti-itherũ, inyuĩ nĩ inyuĩ riiri witũ na gĩkeno giitũ.
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1.177548
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10 Yeesu wi'i ɓe: "Gaddee liƴƴi ɗi nanŋuɗon ɗin."
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10Nake Jesũ akĩmeera atĩrĩ, "Rehei thamaki imwe cia iria mwagwatia."
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1.177294
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O ɗon yaaɓa dow towɗe duniyaaru.
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Thĩ nĩĩhaandĩtwo ĩkarũma ki,
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1.177079
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be contrary to, be diffident, be doubtful, be dubious,
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Ûgeetha ûteithio na itherero, ûkarora na rûgûrû,
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1.176479
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24 Ammaa Yeesu wedditanaakiɓe, ngam e mo andi ngikku neɗɗo.
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24No Jesũ ndaigana kũmeĩhokera, nĩgũkorwo nĩooĩ andũ othe.
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1.176109
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NGAM ɗume Yeesu wari lesdi?
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Nĩ kĩĩ gĩatũmĩte Jesu oke gũkũ thĩ?
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1.175984
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2 Nde ɗonno wondi bee Allah diga fuɗɗam.
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2Kuuma o kĩambĩrĩriainĩ ũcio Kiugo harĩ Ngai.
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1.175234
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O wi'i, "Ee, Joomiraawo, a andi e mi yiɗumaa."
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Nake Jakubu agĩcookia atĩrĩ, "Nĩgeetha njĩtĩkĩrĩke maitho-inĩ maku, wee mwathi wakwa."
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1.174779
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12Nanee belɗum, ngamee, sabu laada ɗuuɗka e reenti on toon dow, gam noon ɓe torrirnoo annabo'en aartuɓe on.
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12Kenai na mũcanjamũke, tondũ kĩheo kĩanyu nĩ kĩnene igũrũ, nĩgũkorwo ũguo noguo maanyariirire anabii arĩa maarĩ kuo mbere yanyu.
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1.174166
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On peway, kan nun laatiimi, 14 jon kan mi maalan nun, mi moodibbo bo, to mi lootii koyɗe mooɗon, onon bo oon kaani lootoontiron koyɗe.
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14Na rĩrĩ, kuona atĩ Niĩ, Mwathani na Mũrutani wanyu, nĩndamũthambia nyarĩrĩ-rĩ, o na inyuĩ nĩmwagĩrĩirwo mũthambanagie nyarĩrĩ mũndũ na ũrĩa ũngĩ.
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1.174163
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1 Yeesu e nder yaadu, sey o yi'i neɗɗo danyaaɗo e wumi.
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1Na rĩrĩa aathiiaga na njĩra-rĩ, akĩona mũndũ waciarĩtwo arĩ mũtumumu.
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1.174139
|
43 Ko waɗi de on paamataa haala am?
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43Mũraga gũtaũkĩrwo nĩ ũrĩa ndĩroiga nĩkĩ?
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1.173973
|
wanna be me? wanna be you
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I wanna be ME ME ME
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1.173968
|
1 Ammaa Yeesu yahi Hocceere Nde Leɗɗe Jaytun.
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1No rĩrĩ, Jesũ agĩthiĩ Kĩrĩma-inĩ kĩa Mĩtamaiyũ.
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1.17357
|
53 Ɓaawo ma'ajum, koo moye fuu hooti wuro muuɗum.
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53 (O mũndũ akĩinũka gwake mũciĩ.
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1.173102
|
O jooɗii emo ekkitina ɓe.
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Nake agĩikara thĩ kũmaruta.
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1.173099
|
Pukara'en maako ngari haa maako.
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Arutwo ake magĩthiĩ harĩ we.
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1.173013
|
woe be onto mankind
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Nĩguo andũ magũkumie,
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1.172916
|
53 On njaɓi tawreeta Allah ka wari bee juuɗe malaa'ika'en, ammaa on tokkaaki ka."
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Na rĩu nĩmũmũkunyanĩire na mũkamũũraga; 537:53 Agal 3:19o inyuĩ mwamũkĩrire watho ũrĩa waathanirwo kũgerera araika no mũkĩaga kũwathĩkĩra."
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1.17264
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To gite maaɗa laaɓɗe, ɓandu maaɗa fuu ɗon nder annoora.
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Maitho maku mangĩkorwo marĩ mega, mwĩrĩ waku wothe nĩũrĩiyũragwo nĩ ũtheri.
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1.171666
|
Noy ngeendam Yeesu fuɗɗiri?
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Ũtũũro wa Jesu waambĩrĩirie atĩa?
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1.171541
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37 Woɓɓe maɓɓe mbi'i: - Imo woodunoo baawɗe wumtinde bumɗo, de o waawaa haɗude Laajaru maayde naa?
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Amwe maigua mweretho ũcio makĩũranagia atĩrĩ, mũndũ ũyũ ũkaigĩrĩrwo maitho nĩ mũtumia wa gathukĩrĩ, nĩ we mũhirani wa ihu?
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1.17107
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25 Eber woodi ɓiɓɓe worɓe ɗiɗo.
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25Eberi nake nĩaciarĩirwo ariũ eerĩ:
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1.170987
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1 Faamu ɗum ɗo: ley balɗe cakitotooɗe wakkatiiji tiiɗuɗi ngaɗan.
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3 No menya ũndũ ũyũ, atĩ matukũ-inĩ ma kũrigĩrĩria+ nĩ gũkaagĩa mahinda ma ũgwati maritũ kũhiũrania namo.
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in Data Studio
Fulah-Kikuyu_Sentence-Pairs Dataset
This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks.
This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: OPUS - NLLB-v1
Metadata
- File Name: Fulah-Kikuyu_Sentence-Pairs
- Number of Rows: 27116
- Number of Columns: 3
- Columns: score, Fulah, Kikuyu
Dataset Description
The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns:
score: The similarity score between the two sentences (range from 0 to 1).Fulah: The first sentence in the pair (language 1).Kikuyu: The second sentence in the pair (language 2).
This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning.
References
Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications:
[1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017
[2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018.
[3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018
[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018.
[5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018.
[6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018.
[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019.
[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761.
[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
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Size of downloaded dataset files:
3.88 MB
Size of the auto-converted Parquet files:
2.85 MB
Number of rows:
27,116