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
| Swahili
stringlengths 1
500
⌀ | Zulu
stringclasses 0
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1.249604
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Je, unajua watoto wote wanapaswa kutii nani? - Ndiyo, baba na mama.
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1.249384
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Yote ni juu yako, Yesu au la.
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1.248955
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□ Ningependa kujifunza Biblia nyumbani kwangu bila malipo.
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1.248932
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Naye aliyeshuka ndiye yeye aliyepaa juu sana kupita mbingu zote, ili avijaze vitu vyote.
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1.248931
|
Ndiyo, kila 30s auto upya tena
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1.24892
|
Matawi yote ya magharibi yanapaswa kuisikiliza sauti Yangu:
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1.247896
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'Je, Yesu angefurahia kutazama filamu hii au kusikiliza muziki huu?
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1.247895
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Kwa nini tusikilize mashauri yao?'
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1.247711
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Lakini je, wao ni matajiri kumwelekea Mungu? - La, wao ni kama yule tajiri ambaye alimsahau Mungu.
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1.247427
|
Kwa sababu tunapenda kile ambacho Google inafanya.
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1.246061
|
Na Mwenyezi Mungu humkusudia kumpa rehema yake ampendaye.
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1.246
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3 Wanaume na wanawake waliomwogopa Mungu walijua mambo waliyopaswa kufanya.
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1.245742
|
Wamelikataa neno la Yehova, nao wana hekima gani?"
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1.245126
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Nililia kwa muda mrefu sana." - Tamara.
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1.243276
|
Nilizungumza mengi kuhusu Hitler.
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1.243262
|
Zaidi ya yote, alipaswa kumuonyesha mama yangu heshima!"
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1.242996
|
Kwa kweli, waume wote ambao hawakuweza kuandamana na wake zao wanakubaliana kwa dhati na maneno hayo!
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1.242879
|
Sababu gani Mungu hakupigania Wayahudi kama vile alifanya wakati wa zamani?
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1.242772
|
Hutaogopa hofu ya usiku,
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1.242719
|
Machi 2013 _ Jinsi ya Kuwa Baba Mzuri
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1.242559
|
Lakini kwa sababu hukuniamini, hutaweza kuzungumza mpaka mtoto huyo azaliwe.'
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1.242392
|
Unafikiri kijana huyo alikuwa nani? - Ndiyo, huenda alikuwa Marko!
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1.242115
|
Mambo haya yamekuwako katika siku zenu, Au katika siku za baba zenu?
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1.24168
|
2, kampuni yetu inaweza kuzalisha Max.
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1.241553
|
Abeli anazungumza nawe leo.
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1.241482
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Mungu mwenye upendo hawezi kuumba mahali kama hapo.'
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1.241453
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Ibilisi pia alizusha suala lingine.
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1.241148
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Nilijifunza mengi ... kuhusu utamaduni wa Uingereza.
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1.24113
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na vitu vyote vinavyoonekana na visivyoonekana.
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1.241071
|
Sasa Amekuja miongoni mwenu kufanya kazi; je, huu si wokovu zaidi?
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1.240805
|
Kumbuka, ni wiki yangu ya tatu kazini. 🙂
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1.240596
|
Hiki ndicho kizazi cha wamtafutao,
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1.240577
|
Ni nini kilitokea huko Houston wiki iliyopita?
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1.24042
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Nilikuwa na maswali mengi kuhusu Mungu na maisha.
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1.240389
|
Ninaweza kuitunza mtoto wa usiku wote!
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1.240047
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No one here speaks Greek Hakuna mtu hapa anaongea kigiriki
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1.239578
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Tena tutaiga imani ya Yosefu na mufano wa Yehova, Baba yake mwenye kusamehe.
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1.239526
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hatujui chochote kuhusu maisha na kifo
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1.239441
|
4 Watu wengi leo wako kama Daudi.
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1.23882
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Mahali Ambapo Utumishi wa Wakati Wote Umeniongoza
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1.238667
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Yeye aliyeungwa na Bwana ni ROHO MOJA.
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1.238165
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"Mimi tu kuamini katika sayansi!"
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1.23811
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15 Bila shaka, hakujawahi kuwa na ndoa kamilifu.
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1.237962
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Nilijifunza mengi ... kuhusu utamaduni wa Uingereza.
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1.237848
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Naye aliyeshuka ndiye yeye aliyepaa juu sana kupita mbingu zote, ili avijaze vitu vyote."
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1.237747
|
Hakuna mwanamume, mwanamke, au mtoto duniani anayeweza kumwona Mungu.
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1.237439
|
Huna sababu ya kuogopa." - Mt.
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1.237079
|
Ni nani hasa aliyemsaidia na kumtegemeza Marko?
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1.237067
|
Solomon Mkubwa Nimewasamehe Wote,
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1.236963
|
Usiache kunifuatilia, Google!
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1.236814
|
Watiwa-mafuta wameonyeshaje kwamba wao ndio "watu kwa ajili ya jina [la Yehova]" leo?
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1.236622
|
Los Angeles: Hatukuweza kusahau Los Angeles.
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1.236566
|
Asanteni kwa kutuandalia video hiyo ya watoto." - Nicole, mwenye umri wa miaka 8.
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1.23656
|
Mungu anatazama watu Wake kwa furaha; wanaweza kujua sauti Yake.
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1.236258
|
Sasa unajua ni miji gani ya Uhispania ambayo unapaswa kutembelea.
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1.236236
|
Mnajua kuwa zote hizi ni baraka Zangu, sivyo?
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1.236206
|
"Nikamwita mwanangu atoke Misri" (1)
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1.236181
|
Basi, twaona kwamba hawakuingia huko kwa sababu hawakuamini.
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1.235998
|
Sasa haki imejibu, angalau nchini Canada.
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1.235898
|
Hawakuruhusiwa kurudi katika familia ya Mungu ya malaika waadilifu.
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1.235736
|
Kwa kweli nimepata suluhisho lako katika dakika 10 za kwanza!
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1.235389
|
Umesahau angalau mmoja wao, sivyo?
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1.235259
|
Japani ni nyumba yangu ya pili.
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1.235031
|
Ninataka kumjua atakapokuwa mkamilifu."
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1.23493
|
Itakuwa siku ya hukumu kwa watu wote.
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1.234879
|
Habari juu ya Haruni na kifo cha watoto wake inaweza kutufundisha nini?
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1.23477
|
Je, mitume hao waaminifu waliendelea kufanya hivyo mpaka mwisho wa maisha yao duniani?
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1.234689
|
"Nilizungumza na Alchemy kila siku.
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1.234624
|
Kwa kweli, inaweza hata kwenda moja kwa moja juu ya Youtube!
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1.234618
|
Labda utapata marafiki wapya hapa, kwa nini?
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1.234488
|
Mimi na Sandra tuna furaha zaidi kuliko awali!"
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1.234454
|
3 Wanaume na wanawake waliomwogopa Mungu walijua mambo waliyopaswa kufanya.
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1.234349
|
Unajua inamaanisha nini kuwa mwanafunzi wa Yesu? - Inamaanisha mambo mengi.
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1.233787
|
Wateja wetu wanahitaji majibu... sasa.
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1.233442
|
Lakini aliyejiunga na Bwana huwa roho moja naye.
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1.233358
|
Ukionyesha imani kama ya Noa, wewe pia unaweza kujiokoa pamoja na wapendwa wako.
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1.233293
|
Wana wa Noa walituwekeaje mfano mzuri sana?
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1.23323
|
Wala usiangalie bahari kwa sababu inajua kila kitu
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1.233074
|
2 Ni kwa njia gani Mungu "alitupenda sisi kwanza"?
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1.233045
|
Nilijua alichotaka." - DYLAN.
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1.233001
|
Agosti 2013 _ Unaweza Kuishi Muda Mrefu Kadiri Gani?
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1.232888
|
"Binti yangu mwenye umri wa miaka 11 hapendi kutazama habari.
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1.232772
|
Walifanya maendeleo baada ya muda mfupi na kuwekwa rasmi kuwa watumishi wa huduma."
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1.232652
|
Hata hivyo, kama Paulo alivyowakumbusha, Mungu ndiye chanzo cha mvua na majira yenye kuzaa.
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1.232646
|
Hatukuleta wokovu duniani,
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1.232455
|
Mungu anataka waabudu wake vijana kwa wazee wafanye nini?
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1.232198
|
Bonus itakuwa tu kukusaidia wakati mwingine.
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1.231989
|
Anasali kwa Mungu wake mara tatu kila siku.'
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1.231918
|
Naye amekuwa akifanya hivyo! - 2 Tim.
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1.231863
|
Kama ilivyo na kisa cha Danieli, wapinzani wa siku hizi wa watu wa Mungu wamefanya nini?
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1.231859
|
Ningehitaji nini?' - 1 Pet.
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1.231736
|
Lakini yeye aliyeungwa na Bwana ni roho moja naye. "
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1.231602
|
Martha, unaamini jambo hili?'
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1.231594
|
Nilisema hivi: "Katie, nitafanya yote niwezayo.
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1.231319
|
Mwanangu na binti yangu walikataa kujaribu moja.
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1.231296
|
Je, mitume hao waaminifu waliendelea kufanya hivyo mpaka mwisho wa maisha yao duniani?
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1.231133
|
Mama mmoja nchini Japani alifanya hivyo.
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1.231065
|
Kwa siku nne alizokuwa amekufa 'hakujua lolote kamwe.'
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1.230954
|
Kulingana na andiko hilo, je, Mungu anaona watu wake wanapoteseka?
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1.230705
|
Tuna ujuzi sahihi wa Neno lake na tunaelewa vizuri ukweli kumhusu na kuhusu makusudi yake.
| null |
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Swahili-Zulu_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: Swahili-Zulu_Sentence-Pairs
- Number of Rows: 1366197
- Number of Columns: 3
- Columns: score, Swahili, Zulu
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).Swahili: The first sentence in the pair (language 1).Zulu: 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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Number of rows:
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