Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele
Idanwo Awoṣe Ede boju-boju
Understand masked language modeling, BERT’s token-selection scheme and later alternatives for bidirectional pretraining.
Ibeere 1 ti 8
Kini iṣẹ ikẹkọ mojuto ni awoṣe ede ti ko boju mu?
Awotẹlẹ gbogbo ibeere8 ibeere
Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.
- Kini iṣẹ ikẹkọ mojuto ni awoṣe ede ti ko boju mu?
- Ṣe asọtẹlẹ awọn ọrọ ti o farapamọ mọọmọ lati agbegbe agbegbe
- Asọtẹlẹ ọrọ ti o tẹle lẹhin gbolohun kan
- Titumọ ọrọ laarin awọn ede meji
- Awọn gbolohun ọrọ ipo nipasẹ atunse girama
- Ni aijọju kini ogorun awọn ami-ami ni BERT nigbagbogbo boju-boju lakoko ikẹkọ iṣaaju?
- Nipa 50%
- Nipa 15%
- Nipa 1%
- Nipa 90%
- Kini idi ti MLM ṣe fun oye bidirectional awoṣe?
- O ka awọn gbolohun ọrọ sẹhin
- O nlo awọn awoṣe lọtọ meji
- Àmi kọọkan ṣe deede si awọn ọrọ ni apa osi ati ọtun
- O kan wo ọrọ ti tẹlẹ
- Ninu ero iboju BERT, kini o ṣẹlẹ si bii 10% ti awọn ipo ti a yan dipo di [MASK]?
- Wọn ti parẹ patapata
- Wọn ti wa ni pidánpidán
- Wọn jẹ titobi nla
- Wọn ti wa ni rọpo pẹlu a ID ọrọ
- Lori awọn ipo wo ni a ṣe iṣiro pipadanu MLM?
- Nikan ni boju awọn ipo
- Gbogbo àmi ninu gbolohun ọrọ
- Nikan akọkọ ati ki o kẹhin àmi
- Nikan aami ifamisi
- Ti a ṣe afiwe pẹlu asọtẹlẹ ami atẹle, kini MLM ṣe iṣowo kuro?
- Agbara lati lo ọrọ-ọrọ
- Agbara lati ṣe ipilẹṣẹ ọrọ ti o ni irọrun si osi-si-ọtun
- Wiwọle si a fokabulari
- Lilo akiyesi
- 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
- 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