T5 uye Text-to-Text Transfer Mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeipi iyo inotsanangura pfungwa kumashure kweT5?
   • Inongoshandura muchina chete
   • Inogadzira mifananidzo kubva kune zvinyorwa
   • Basa rega rega reNLP rakamisikidzwa sekupinza kwemavara kune kubuda kwemavara
   • Inotsiva kudzidziswa kwese nekusimbisa kudzidza
2. Ndeipi Transformer architecture inoshandiswa neT5?
   • A full encoder-decoder
   • Encoder-chete, seBERT
   • Decoder-chete, seGPT
   • Hapana kana hanya zvachose
3. Ndechipi chinangwa chepretraining icho T5 inonyanya kushandisa?
   • Kufanotaura kwemutsara unotevera chete
   • Kufanotaura mitengo yemasheya
   • Image inpeinting
   • Span huwori, masking contiguous spans inotsiviwa nesentinel tokens
4. Ko T5 yakafanodzidziswa pairi corpus?
   • Wikipedia chete
   • Iyo C4 Colossal Yakachena Inokambaira Corpus
   • A dataset of source code
   • Zvinyorwa zvekuteerera
5. Ko T5 inomiririra sei basa rekuisa senge ongororo yemanzwiro?
   • Inoburitsa probability vector chete
   • Inodzosa ID yenhamba
   • Inogadzira izwi chairo rezita, senge 'positive' kana 'negative'.
   • Haikwanise kuita classification
6. Chii chinonzi FLAN-T5 inoenderana neyekutanga T5?
   • Mudiki mufananidzo modhi
   • Chizvarwa chakarongeka chinokonzeresa mabasa asingaonekwe
   • Chimiro chekuziva kutaura
   • Chishandiso chedhatabhesi chisingaenderane
7. When T5 generates a summary, what does decoder cross-attention let it consult?
   • The encoder's representations of the input article
   • Only previously generated summary tokens
   • Only the unprocessed task label
   • The training optimizer's stored gradients
8. In a closed-book T5 question-answering setup, where does the answer's factual information come from at inference?
   • A document retrieval step required for every question
   • A newly supplied answer passage required in the prompt
   • A live database lookup built into span corruption
   • Knowledge encoded in the model's learned parameters