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
score
float32 1.04
1.25
⌀ | Shona
stringlengths 9
500
⌀ | Swahili
stringlengths 6
500
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1.249971
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Pane zvakawanda kwazvo norudo pamusoro Paris.
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Kuna mambo mengi kwa upendo kuhusu Paris.
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1.249186
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Naizvozvo Mwari anonzwira ngoni waanoda kunzwira ngoni, uye anoomesa mwoyo wouyo waanoda kuomesa.
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Lakini Mwenyezi Mungu humkunjulia riziki amtakaye, na humdhikisha
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1.248397
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Vanhu vevanakomana vaIsraeri vawanda uye vava nesimba kutipfuura.+
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Watu wa wana wa Israeli ni wengi zaidi na wenye nguvu kuliko sisi.
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1.248262
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Ndainyatsoziva zvaaida." - DYLAN.
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Nilijua alichotaka." - DYLAN.
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1.247535
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"Baba venyu vanoziva kuti zvinhu zvipi zvamunoda." - MAT.
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"Baba yenu anajua ni vitu gani mnavyohitaji." - MT.
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1.247425
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Kana ukava mumamiriro ezvinhu akadaro, funga muenzaniso waMosesi.
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Ikiwa uko katika hali moja kati ya hizo, fikiria mufano wa Musa.
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1.247335
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Ndinoda kuva munhu akaita sei? - VaKorose 3:10.
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Ninataka kuwa mtu wa aina gani? - Wakolosai 3:10.
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1.247244
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Hama nehanzvadzi dzakawanda pasi rese dziri kutinyengeterera."
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Ndugu na dada katika dunia yote wanasali kwa ajili yetu."
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1.247201
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4 Mazuva ano kune vakawanda vakaita saDhavhidhi.
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4 Watu wengi leo wako kama Daudi.
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1.247139
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Zvadaro, tichaona kuda kwaMwari kuchinyatsoitwa panyika, mabhiriyoni evanhu paachamutswa kuti agare munyika inenge yava paradhiso.
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Kisha, tutaona mapenzi ya Mungu yakitendeka kikamili mabilioni ya wafu watakapofufuliwa katika paradiso duniani.
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1.247017
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(April 1, 2012); "Vanhu Vose Vakanaka Vanoenda Kudenga Here?"
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(Aprili 1, 2012); "Je, Watu Wote Wazuri Huenda Mbinguni?"
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1.246959
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Vanhu vevanakomana vaIsraeri vawanda uye vava nesimba kutipfuura.'
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Watu wa wana wa Israeli ni wengi zaidi na wenye nguvu kuliko sisi.'
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1.246628
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Iye zvino imi nevana venyu mava kufanira kubatsirawo vanhu vomuFrance!
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Kwa hiyo, ninyi na watoto wenu mnapaswa kuwasaidia Wafaransa!
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1.246512
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Muchitsauko 4 akataura nezvaAbrahamu.
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Katika sura ya 4, alizungumza kuhusu Abrahamu.
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1.245798
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"[Mwari haasi] kure nemumwe nemumwe wedu."
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"Kwa hakika, [Mungu] hayuko mbali na kila mmoja wetu."
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1.245776
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Ndiine vabereki vangu muna 2003
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Nikiwa na wazazi wangu, mwaka wa 2003
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1.245768
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Ndatenda, Daniel, chiizve chekutaura?
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Asante, Daniel, ni nini kingine cha kusema?
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1.244632
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Asi Mwari anopikira kuti: "Ini handingakukanganwi."
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Lakini Mungu anaahidi: "Mimi mwenyewe sitakusahau."
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1.244574
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Kubvira ipapo, ndiri kushumira semumishinari muJapan."
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Tangu wakati huo, nimekuwa nikitumikia nikiwa mmishonari nchini Japani."
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1.244465
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Los Ángeles: Hatina kukwanisa kukanganwa Los Angeles.
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Los Angeles: Hatukuweza kusahau Los Angeles.
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1.243769
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Ndinoda kuti unakidzwe neupenyu semumwe weZvapupu zvangu!"
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Ninataka ufurahie maisha ukiwa mmoja wa Mashahidi wangu!"
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1.243469
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Ini ndangotumira $ 1000 kuna Noah!
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Nimetuma tu $ 1000 kwa Noah!
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1.243381
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Uri Kuwana 'Zvokudya Panguva Yakakodzera' Here?
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Unapata Chakula "kwa Wakati Unaofaa"?
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1.24321
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Akabva angoziva vakoma vake!
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Aliwatambua ndugu zake mara moja!
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1.242559
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"Nokuti Latvia zvakare?"
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"Kwa Latvia tena?"
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1.242126
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Unofunga kuti dzinotaurawo nezvedu here? - Hungu, dzinodaro.
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Je, unafikiri wao pia huzungumza juu yetu? - Ndiyo.
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1.241754
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Kumutswa kwevakarurama nevasina kururama kuchaitika rini?
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Ufufuo wa waadilifu na wasio waadilifu utatukia lini?
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1.24142
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Iye akati: "Unogona kutaura chiGiriki here?
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Akasema: "Je, unaweza kusema Kigiriki?
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1.241111
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Ndaingova nemakore 10." - Elisa.
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Nilikuwa tu na miaka kumi." - Elisa.
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1.241095
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Mwari anosarudza waanoda kuti ave Muporofita.
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Lakini Mwenyezi Mungu humteua katika Mitume wake amtakaye.
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1.241002
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London chaizvoizvo ane zvose!
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London kweli ana kila kitu!
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1.240918
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Kubvira mugore ra1931 tave tiine ropafadzo ipi yakazonaka?
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Tumekuwa na pendeleo gani la pekee tangu mwaka wa 1931?
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1.240884
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Switzerland yakandibatsira zvakanyanya mubasa rangu kupfuura France. "
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Uswisi imenisaidia mengi zaidi katika kazi yangu kuliko Ufaransa. "
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1.240607
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Muprofita wacho ndiye aizova "mwana" akavimbiswa.
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Nabii huyo angekuwa ule "uzao" ulioahidiwa.
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1.240495
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Kusvika pari zvino tadzidza Bhaibheri nevanhu 112 vechiChinese!
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Kufikia sasa, tumejifunza Biblia na Wachina 112!
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1.240308
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2 Abheri anotaura newe nhasi.
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2 Abeli anazungumza nawe leo.
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1.240092
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3 Varume nevakadzi vaitya Mwari vaiziva zvavaifanira kuita.
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3 Wanaume na wanawake waliomwogopa Mungu walijua mambo waliyopaswa kufanya.
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1.240075
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Ungatevedzera sei muenzaniso waJosefa kana vamwe vakakubata zvisina kunaka?
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Namna gani unaweza kufuata mufano wa Yosefu wakati wengine wanakutendea mubaya?
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1.239682
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Ari wechitatu uye ini handisi kupa darn! "
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Yeye ni wa tatu na mimi si kutoa darn! "
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1.239402
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Ndiani anogona kuona zvese zvatinoita?
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Ni nani anayeweza kuona kila kitu tunachofanya?
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1.239367
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Muupenyu hwavo vakataura kwamuri!
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Katika maisha yao aliongea na wewe!
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1.239255
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Ndiri muchengeti wemunin'ina wangu?"
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Kwani mimi ni mlinzi wa ndugu yangu?"
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1.239233
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Semuenzaniso, munoedza kuvimbika pazvinhu zvese here?
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Kwa mfano, je, wewe hujitahidi kuwa mnyoofu katika mambo yote?
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1.239177
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Vakarara, vachimirira kumutswa.
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Wanalala huku wakingojea ufufuo.
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1.239124
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Tichave tichinamata zvinoenderana nemagwaro (izwi raMwari) nekuti Anosimudzira mazwi Ake kupfuura zita rake.
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Tutakuwa tunaomba kulingana na maandiko (neno la Mungu) kwa maana Yeye huinua maneno yake kuliko jina lake.
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1.239033
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Ndiri muchengeti wemunin'ina wangu?"
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Je, mimi ni mlinzi wa ndugu yangu?"
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1.238797
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10 Hatisiwo "venyika."
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10 Sisi pia "si sehemu ya ulimwengu."
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1.238777
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"Ndinofarira mutemo waMwari." - VAR.
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"Kwa kweli naipenda sheria ya Mungu." - ROM.
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1.238454
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Mwari wenyu achauya kuzotsiva,
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Na hakika Mola wako Mlezi ndiye atakaye wakusanya.
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1.238127
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Ndava kunyatsofara." - Karen.
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Nina furaha sana." - Karen.
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1.237943
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Roti akazvitsaurira bani rose reJorodhani; Roti akaenda kurutivi rwamabvazuva; vakapa radzana havo.
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Basi Lutu akajichagulia bonde lote la Yordani; Lutu akaenda upande wa mashariki, wakatengana wao kwa wao.
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1.237849
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Kubvira kare, vaya vanoda Shoko raMwari vanoziva izvozvo.
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Tangu zamani wale wenye kupenda Neno la Mungu wametambua jambo hilo.
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1.237629
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Ipapo ndichapindura mubvunzo wamambo."
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Kisha nitajibu swali la mfalme."
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1.237623
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Vakayeuka Musiki Wavo Mukuru
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Anakumbukira Mlengi Wawo Wamkulu
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1.237567
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Japan ndiyo imba yangu yechipiri.
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Japani ni nyumba yangu ya pili.
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1.23734
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17 Kufamba nezita raMwari kunosanganisira zvinhu zvinenge zvitatu.
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17 Kutembea katika jina la Mungu kunahusisha angalau mambo matatu hivi.
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1.237201
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Mwari ane rudo haangambosika nzvimbo yakadaro.'
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Mungu mwenye upendo hawezi kuumba mahali kama hapo.'
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1.236698
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Asi akanga asina "kupfuma kuna Mwari."
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Lakini hakuwa "tajiri kwa Mungu."
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1.236328
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Ndaiva nemibvunzo yakawanda pamusoro paMwari uye upenyu.
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Nilikuwa na maswali mengi kuhusu Mungu na maisha.
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1.236208
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Tinoziva kuti Mwari haanzwi vatadzi.
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Tunajua kwamba Mwenyezi Mungu Hawasikilizi wenye
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1.236069
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Ichi ndicho chikonzero iwe uchifanira kutsvaga Mwari mangwanani-ngwanani.
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Hii ndiyo sababu lazima utafute Mungu mapema asubuhi.
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1.236057
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Anofara munhuwo zvake anoita izvi."
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Mwenye furaha ni yule ambaye anafanya jambo hili.'
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1.235968
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Tinogona kutaura naMwari kwenguva refu, panzvimbo yatinenge tada uye chero nguva.
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Tunaweza kuongea na Mungu kwa muda mrefu pindi yoyote na mahali popote.
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1.235914
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Mwari akatsanangura zvinoitika kana tichinge tafa paaitaura naAdhamu, munhu wokutanga.
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Mungu alieleza kile kinachotokea tunapokufa aliposema na mwanadamu wa kwanza, Adamu.
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1.235726
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Wakambonyepa here? - * Kunyange vamwe vanhu vakuru vaida Mwari vakambonyepawo.
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Je, umewahi kudanganya? - * Hata baadhi ya watu wazima wanaompenda Mungu wamewahi kudanganya.
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1.235487
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Tinobva taedza kuita nguva yakawanda nehama nehanzvadzi."
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Kisha tunajikaza kupitisha wakati mwingi zaidi pamoja na ndugu na dada."
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1.235349
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Ndivanaani vanamati vaMwari vechokwadi panyika mazuva ano?
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Ni nani walio waabudu wa kweli wa Mungu duniani leo?
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1.235262
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Kubvira zvino uchange uchibata vanhu vari vapenyu."
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Tangu sasa na kuendelea utakuwa ukiwavua watu wakiwa hai."
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1.23521
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36 Kushanda chete. / Ita basa rako pachako.
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36 Kazi peke yake. / Kazi yako mwenyewe.
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1.235129
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(Genesi 15:15) Uye izvi ndizvo chaizvo zvakaitika.
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(Mwanzo 15:15) Na hivyo ndivyo ilivyokuwa.
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1.235102
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Funga nezvaDennis * uyo aishumira semukuru.
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Fikiria mfano wa Dennis * aliyekuwa akitumikia akiwa mzee.
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1.235061
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Aitenda kuti vakafa vachamutswa.
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Aliamini ufufuo.
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1.234982
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Imi muri vana vechiedza nevana nhasi.
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Wewe ni wana wa nuru na wana wa leo.
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1.234631
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Ndava kunyatsofara." - Karen.
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Mimi ni mwenye furaha sana." - Karen.
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1.234517
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(Taura zita remunhu wacho) uye bvisa chero mibvunzo.
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(Sema jina la mtu huyo) na ubadilishe maswali yoyote.
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1.234408
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Vaitishanyira kumba kwedu kuti vaone kana taida kubatsirwa."
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Walitutembelea nyumbani ili kuona ikiwa tulihitaji msaada."
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1.234321
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Anofara munhuwo zvake anoita izvi."
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Mwenye furaha ni yule mwanadamu ambaye hufanya jambo hili.'
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1.23425
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Mwedzi wako wekutanga wave patiri!
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Mwezi wako wa kwanza uko juu yetu!
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1.234127
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Wava kuona here zvino nei zvakaoma kwazvo kuita zvakanaka? - Satani nenyika yake vanoita kuti zviome.
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Unaona sasa ni kwa nini si rahisi sana kutenda mema? - Shetani na ulimwengu wake wanafanya iwe vigumu.
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1.233878
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Ndiri kudzidza chiGerman muFrance nhasi.
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Ninajifunza Kijerumani nchini Ufaransa leo.
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1.233859
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Zvechokwadi, inogona kunyange kuenda zvakananga pane Youtube!
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Kwa kweli, inaweza hata kwenda moja kwa moja juu ya Youtube!
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1.233826
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Mazuva ano kune vechidiki vakawanda vakafanana naTimoti.
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Leo, kuna vijana wengi walio kama Timotheo.
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1.233735
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Ndaingova nemakore 10." - Elisa.
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Nilikuwa na umri wa miaka kumi tu." - Elisa.
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1.233713
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Chaizvoizvo, muchitsauko 4, ndakakudzidzisa kuti ungazviita sei.
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Kwa kweli, katika sura ya 4, nilikufundisha jinsi ya kufanya hivyo.
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1.233678
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Tichavasiya kusvikira vave ndarama.
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Tutawaacha mpaka wawe dhahabu.
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1.233585
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Mumwe nemumwe "wevaduku ava" anokosha kuna Mwari.
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Kila "mmoja wa wadogo hawa" ni mwenye thamani kwa Mungu.
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1.233549
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Ko, zvakadai zvakatomboitika here pamazuva enyu, kana pamazuva amadzitateguru enyu?
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Hii imewahi kuwa kabla ya siku zenu au katika siku za baba zenu?
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1.233536
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Chokwadi changu chakarasika usiku:
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Ukweli wangu umepotea usiku:
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1.233527
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Taiziva kuti ainyepa." - STEPHEN.
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Tulijua alikuwa akidanganya." - STEPHEN.
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1.233385
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Panguva iyi, iniwo ndakanga ndava kugara kuHong Kong zvekare.
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Wakati huo, mimi pia nilikuwa ninaishi tena Hong Kong.
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1.233333
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Miriam anoti: "Takanzi tishumire semapiyona chaiwo.
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Miriam anasema hivi: "Tuliombwa kutumikia tukiwa mapainia wa pekee.
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1.233214
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Iye ndokuti: Usatya, nekuti avo vanesu vanopfuura vari kwavari kupfuura ivo.
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Akamwambia, Usiogope; maana walio pamoja nasi ni wengi kuliko wale walio pamoja nao."
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1.233176
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Tichaita saDhavhidhi here kana kuti Sauro?
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Je, tutakuwa kama Daudi au kama Sauli?
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1.233131
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Ndokuona zvakare munguva pfupi! -Pauro
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Tutaonana tena hivi karibuni! -Paulo
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1.232901
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Ndinonzwa nani uye akachengeteka zvikuru pano kupfuura chero guta guru iri America kunyange usiku!
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Najisikia salama na salama zaidi hapa kuliko katika mji wowote kubwa katika Amerika hata wakati wa usiku!
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1.232753
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"[Mwari haasi] kure nemumwe nemumwe wedu."
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"Kweli, [Mungu] haiko mbali sana na kila mumoja wetu."
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1.232232
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Vashumiri vangu vachanwa, asi imi muchava nenyota.
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Watumishi wangu watakunywa, lakini ninyi mtakuwa na kiu.
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1.23208
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Kakawanda uyewo akashanyira bonke Sweden.
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Mara nyingi pia alitembelea Bonke Sweden.
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1.232059
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11 Zvaisagona kuitwa naDhavhidhi: Dhavhidhi aisagona kuchinja zvaakanga atoita kare.
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11 Mambo yenye Daudi hangeweza kufanya: Daudi hangeweza kubadilisha mambo yenye alikuwa amefanya.
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1.231918
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Iye zvino ndiri kudzidza nevashanu vavo."
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Sasa, niko najifunza na tano kati yao."
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in Data Studio
Shona-Swahili_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: Shona-Swahili_Sentence-Pairs
- Number of Rows: 1099183
- Number of Columns: 3
- Columns: score, Shona, Swahili
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).Shona: The first sentence in the pair (language 1).Swahili: 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
- Downloads last month
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Size of downloaded dataset files:
186 MB
Size of the auto-converted Parquet files:
132 MB
Number of rows:
1,099,183