Masked Mutauro Modelling Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeipi basa rekutanga rekudzidzisa mune masked mutauro modelling?
   • Kufanotaura mazwi akavanzwa nemaune kubva pane zvakatenderedza
   • Kufanotaura izwi rinotevera mushure memutsara
   • Kushandura mavara pakati pemitauro miviri
   • Kuisa mitsara maererano nekurongeka kwegirama
2. Angangoita chikamu chei muzana yematokeni anoita BERT anowanzo mask panguva yekudzidzira?
   • Zvinenge 50%
   • Zvinenge 15%
   • Zvingangoita 1%
   • Zvinenge 90%
3. Sei MLM ichipa modhi bidirectional kunzwisisa?
   • Inoverenga mitsara ichidzokera kumashure
   • Inoshandisa mhando mbiri dzakasiyana
   • Chiratidzo chega chega chinotarisa mazwi ari kuruboshwe uye kurudyi
   • Inongotarisa izwi rapfuura
4. MuBERT's masking scheme, chii chinoitika kune ingangoita gumi muzana yezvinzvimbo zvakasarudzwa pane kuve [MASK]?
   • Vanodzimwa zvachose
   • Dzakadzokororwa
   • Vakaiswa mazita makuru
   • Dzinotsiviwa neshoko risina kurongeka
5. Ndeapi nzvimbo iyo MLM kurasikirwa kunoverengerwa?
   • Chete nzvimbo dzakavharwa
   • Chiratidzo chese mumutsara
   • Chekutanga uye chekupedzisira zviratidzo
   • Zviratidzo zvepumikira chete
6. Kuenzaniswa nekufanotaura kunotevera, MLM inotengesera chii?
   • Kugona kushandisa mamiriro ezvinhu
   • Kugona kugadzira mavara anotsetseka kubva kuruboshwe kuenda kurudyi
   • Kuwana mashoko
   • Kushandiswa kwekucherechedza
7. How does RoBERTa’s dynamic masking differ from BERT’s original setup?
   • It masks only the first token of every sentence
   • It predicts future tokens with a causal decoder
   • The masked positions can be regenerated across training passes instead of remaining fixed
   • It replaces all masked tokens with random numbers
8. What auxiliary objective did the original BERT pretraining recipe combine with MLM?
   • Image patch reconstruction
   • Audio speaker diarization
   • A reward model for code generation
   • Next-sentence prediction