Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele
Fi agbara mu Olukọ ni adanwo Awọn awoṣe Ọkọọkan
Checks how ground-truth prefixes train a sequence decoder, how causal masks support parallel training, and why inference differs.
Ibeere 1 ti 8
Lakoko ipaniyan olukọ, kini ifunni bi titẹ sii ni igbesẹ iyipada kọọkan?
Awotẹlẹ gbogbo ibeere8 ibeere
Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.
- Lakoko ipaniyan olukọ, kini ifunni bi titẹ sii ni igbesẹ iyipada kọọkan?
- Ilẹ-otitọ ti tẹlẹ àmi
- Awọn awoṣe ile ti ara ti tẹlẹ asọtẹlẹ
- A ID àmi
- Ik o wu àmi
- During teacher-forced training, what target is paired with decoder input y_(t−1)?
- The previous ground-truth token
- The next target token y_t
- A token sampled from the validation set
- The maximum-probability output from an earlier training run
- Aiṣedeede idanwo-irin-irin ti o ṣẹlẹ nipasẹ ipaniyan olukọ ni a mọ si:
- Awọn gradients ti nparun
- Ipo iṣubu
- Iyasọtọ ifihan
- Aami jijo
- Ninu oluyipada Ayipada, ẹrọ wo ni o jẹ ki ikẹkọ fi agbara mu olukọ ṣiṣẹ ni afiwera kọja awọn ipo?
- Jade kuro
- Ipese deede
- Iwadi ina
- Iboju ifojusọna idi kan (wo iwaju).
- Ni akoko itọkasi, kilode ti a ko le lo ipaniyan olukọ?
- Ko si awọn ami-ilẹ-otitọ ọjọ iwaju lati jẹun
- Iṣẹ pipadanu ko si
- GPU ti lọra pupọ
- Awọn iboju iparada ti wa ni alaabo
- Ipadanu wo ni a maa n lo ni igbesẹ kọọkan lakoko ikẹkọ fi agbara mu olukọ?
- Itumọ onigun mẹrin aṣiṣe lori aise logits
- Cross-entropy laarin awọn ti anro pinpin ati otito tókàn àmi
- Mitari pipadanu lori awọn ifibọ
- Àdánù ibajọra Cosine
- The target sequence is “red, blue, green.” Under teacher forcing, which token is supplied when training the prediction of “green”?
- The model’s prediction for “green”
- The future token after “green”
- The ground-truth previous token “blue”
- A randomly sampled vocabulary item
- At decoder position t, which tokens must a causal attention mask block?
- The source sequence
- All earlier target tokens
- The current position’s own input
- Target tokens at positions later than t