texte_wolof stringlengths 3 829k |
|---|
Tey, kenn du jaamloo jammbuur |
Dinaa la ca yóbb. |
Ñaanal ma ndam ! |
Te bu Nu la dëgaralul woon, tuuti kon nga xaw a jeng jëm ci ñoom |
Mba ya ngi ci jàmm ? |
Doonul nag nguur gu yàgg, ci 539 la ko waa-persi yi sanc. |
Mustafa Kemal Atatürk thumb right 300px Mustafa Kemal Atatürk . Mustafa Kemal Atatürk mi ngi juddoo ca Saloniki ci 19 Mee ci atum 1881, génn àdduna ca Istanbul ca Tirki ci 10 Nowembar 1938. Nekkoon taalifkat, bindkat doonoon it killifa aada ca Tirki. .--------------------.Atatuerk.Atatuerk |
Ku weddi, weddeem ga dana dal ca kawam. Ñay jëf yiw nag... Ñoom ña ngay waajal seen bopp [daluwaay bu maase] néeg, |
Bi ñu njëkkee daje ci Ndakaaru, Jaraaf a ko dóoroon 20. |
Nataal bi ngeen ma yónnee de nataal la boo xam ne gis naa ci lu bula ak lu wert, ba noppi mu am ay couleur yoo xam ne benn bokku ci ak moroomam waaw. Ak lu mel ni saari jumbax la daal lay nuru. |
ku sopa doom àm du ko ñaka ratah |
bu sa loxo ubbiku ndax nangu té tëju ndax maye |
Jërëjëf ci màndarga mi. |
muy lem loosam [di rëy-rëylu] ngir réerloo [nit ñi] soreel leen yoonu Yàlla. Kooku dey am na ci àddina toroxtaange ; te Dananu ko mosal Bis-pénc ba mbugal muy tàng di lakkaate. |
Way-dëkk thumb upright 1.5 Suufi Way-dëkk1994.Maanaam ña dëkk ca réew mà ñoom lanuy woowe way-dëkk budee kenn nak aji-dëkk lanu koy wax . .Wàll:Melosuuf.id:Penduduk Penduduk dunia |
bu jëkk la saxaar yéexoon léegi loolu deñ na |
Waxal ne : “Yàlla sama Boroom mooy yaatalal ku ko soob wërsëg, di ko xatal it. Waaye li ëpp ci nit ñi waxuñu”. |
Doomi Senegaal yi nag joxe nañu lu tollu ci fukk ak juróomi milyaar |
Danuy wàññi daamar gi. |
Damaa nekk ci ak daamar waaye buma wàccee dinaa kii bu soobee Yàlla. |
Dafa ame jikko. |
ni njóof di muure jaasi war ma bebb boor |
CAX Di Caabal ak Xarala gi. |
Am naa lekk. |
Masaï yi ca Kenya Engaï |
Bu ñu indi woon sax ñeenti seede, waaye bu ñu ko indiwul [ngeen daldi xam fa saa sa] ne ñoom kat ay fenjat lañu. |
Ndax am nga ag daamar? |
Woowaatleen seen i xaj ! |
Mustafaa Suraŋ yokku na ca ña jot a dem. |
Man ngaa leen a wóolu. |
px Réew yu Bennoo yu Amerig |
Ma ne manoon na caa dem. |
Ku damm toogu gi ? |
Damaa jekki rekk tasee ak moom. |
Xawuñoo eggale seen njàmbat loolu ba Nu def leen ni am ngóobit mu ne selaw. |
Nataal bii mi ngi wone ak nit ku yéeg cib welo. Boo xoolee mi ngi yeb kenn gu jigéen. Li ngi gàddu benn defu ci kanam bu def ay mbir ci biir. |
Ku tuub ginnaaw ba mu tooñee, te mu sellal jëfam, Yàlla dana ko jéggal. Ndax Yàlla Jéggalaakoon la, Jaglewaakoon la. |
dañu fay béel. Ndaw dëkkuwaay bu neex ! |
Tom tegoon na benn dëgg-sërax. |
Dox naa ngir jot ci ag kër. |
Di ay kàddu yu mu yékkati ci RFM. |
Xool Diiney Afrig |
Waxleen Tom mu noppi. |
Ma tëdd fan ? |
am na genn sàppóoti gu nekk ca diggu ëtt ba |
Ndax danoo dàq ? |
Gis naa leen ñoom ñépp. |
Sol Nanu [Nun Yàlla] leerug Yonent ci yaw [Muhammad] ni Nu ko sole woon ca [Nooh] ak Yonent ya toppoon ca moom. Sol Nanu ko Ibraahiima ak Ismaahiila ak Isaaqa ak Yanqooba, ak sët ya ak Iisaa, ak Ayuuba, ak Yuunusa, ak Haaruun, ak Suleymaan, te jox Nanu Daawuda ab Téere bu tudd [Zabuur]. |
Aakimoo jawwu ji, walla xare bi ngir teg loxo ci jawwu ji sanc ko |
Am bukki yomb na, waaye bukki buy xalam a jafe. |
- Neel : “Sama Boroom, Yaw miy Boroom nguur. Yaay jox nguur ku la soob, di rocci nguur ci ku la soob ; Yaay teral ku la soob, Yaay toroxal ku la soob. Yiw wépp a ngi ci sa loxo te kàttanu nga ci def lu ne. |
Peggu ndox ak suuf la ñuy woowee tefes. |
bër set mu daldi fàqlu lu dooni yari daqaar laxasante ko |
Damaa tëju ci biti. |
Dangaa yàkkamti ba. |
Yirim Ndaten Buubu |
man naa wax siinwaa. |
Man naa noppalu benn pan a? |
dañu mànkoo ne dootuñu wax ak moom |
Kawlak ab selebiyoon bu mag la |
Teg Nanu nitu Firawna bekkoor ay at ak wàññikug meññent ndax xéy-na ñu fàttaliku [seen Boroom]. |
Nguurug Piemonte ak Bennalug Itaali |
gaa ñi daldi fetal sox yi till gi daldi daanu |
Dinaa la woo ci kanam. |
Ndax say yos la? |
Manu leen koo sikk. |
Bëtub mbëggeel du gis melo |
Fa mu sëgg di metitlu. |
Nun daal, kenn dunu mbugal”. |
Xam na li nekk ci seen kanam ak la weesu nekk ca seen ginnaaw. Te ca Yàlla la mbir yépp di dellusi. |
Anam bi muy soppeeku dafa gaaw te jéggi dayo |
Gisal ab fajkat ! |
Xeet Iniwersite bu Senegaal |
Koolute gi nag di secretariat bi, ci dëkkub Geneve bi nekk Siwis la nekk |
kaasu meew, bés bu ne. |
Ndax nangu nañu ko? |
Lan moo dal sunu mbokki réewum Afrig-bëj-Saalum ? Su ma nee ‘’mbokk’’, nit ñu ñuul ña fa fekk baax a may tax a wax. Nook nun benn lañu, koo ci ni woon tey diw mii Simbabwe la bokk walla Somali walla Niseryaa, kenn du la weddi. Ci lu amul benn werante, nooy nun, nun nooy ñoom. Nun ñépp la sunu der ñuul, sunuy aada doon ... |
Weer wii di ñëw laay dem. |
Gaal Jiloor giy doxal yaaleg ndefka yi diggante Sigicoor ak Ndakaaru. |
Maa fa jiitu woon. |
Fab jox nu ndam lii |
Loo wax ci benn kaasu sàngara? |
Leeraay bi dafa ëpp. |
Ku fi naan jox sa morom naan! |
Jamono ju bees ja ba fippug waa-frãs ci 1789 |
Ginnaaw bi aji ligeey ji far li mu bindoon, ñu neeti ko bindal |
Jëlleen ci yii benn ! |
Tom dimbali na. |
Nu may ko Isaaqa ak Yàqooba, muy ndollent, Nu def leen ñu nekk ñoom ñépp di ñu sell. |
Jollasu yi neex nañu la. |
bu ngeen paree ñu gam gamle seeni liggéey |
Wan waxtu la ndéego li ? |
ci ñeeloon Yonent bi kem sunu tolluwaay. |
Damaa bëgg ub lolli. |
xeet la ci juddalkaayu mbëj |
Samag dund jeex na. |
Buy déllusi yal na nu fekk ak topp Yàlla gu wér |
Li nga beqi tamit manees na koo nangu. |
Dom yoyu di dundé ñaam bi ci biir,bañu magg |
Danoo bëgg a jóge fii. |
End of preview. Expand in Data Studio
📚 Wolof Text Dataset
🎯 What you can do with this Wolof text data
🤖 Language Model Pre-training
- Train a BERT, GPT or LLaMA model specific to Wolof
- Create word embeddings (Word2Vec, FastText)
- Fine-tune multilingual models (mBERT, XLM-RoBERTa)
📝 Language Modeling
- Next Token Prediction
- Masked Language Modeling (MLM)
- Perplexity calculation
✍️ Text Generation
- Automatic text generation in Wolof
- Sentence completion
- Chatbots and virtual assistants in Wolof
🔤 Tokenization & Analysis
- Create Wolof-specific tokenizers
- Morphological analysis
- Sentence segmentation
🌐 Machine Translation
- Data for Wolof ↔ French/English models
- Neural machine translation training
🏷️ Text Classification
- Sentiment analysis
- Topic categorization
- Language detection
🔍 Information Extraction
- Named Entity Recognition (NER)
- Keyword extraction
- Automatic summarization
📥 Usage
from datasets import load_dataset
dataset = load_dataset("Lahad/wolof_only-dataset")
print(dataset)
- Downloads last month
- 7