Kediska Modeling Permutation XLNet

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay ujeeddada tababbarka ka hor ee ay XLNet isticmaasho?
   • Qaabaynta luqadda Permutation
   • Qaabaynta luqadda madmadow
   • Ogaanshaha calaamada la beddelay
   • Saadaasha jumlada xigta
2. Waa kuwee daciifnimada BERT ee XLNet si gaar ah loogu talagalay inay wax ka qabato?
   • BERT aad buu u yaraa
   • Calaamadda [MASK] waxay sababtaa tabobar hore/habayn-ku-habboon oo is-khilaafsan oo waxay u qaadanaysaa madax-bannaanida calaamad-masabka
   • BERT way xamili kari wayday weedho gaagaaban
   • BERT waxa ka maqan codayn meeleed
3. Maxaa kala saaraya is-fiirsiga labada-qulqul ee XLNet?
   • Lakabyada codeeyaha iyo furaha
   • garaafyada tababarka iyo ka-fiirsashada
   • durdur nuxur leh iyo qulqul weydiin
   • Calaamadaha iyo jumladaha gundhigga
4. XLNet jir ahaan ma isku shaandhaynta erayada jumlada?
   • Haa, si aan kala sooc lahayn ayay dib u dalbaysaa calaamadaha dhabta ah
   • Haa, laakiin kaliya inta lagu jiro hagaajinta
   • Maya, marna ma beddeleyso dalabaad kasta
   • Maya, waxa ay wax ka taraysaa nidaamka habaynta iyadoo la isticmaalayo waji-xidhka dareenka iyadoo la ilaalinayo boosaska
5. Nashqadadee ayaa u adeegta sida laf dhabarta XLNet ee macnaha guud ee fog?
   • Transformer-XL
   • LSTM
   • Transformer vanilj asalka ah
   • Shabakadda kacaanka
6. How does XLNet combine bidirectional context with autoregressive prediction?
   • It predicts all tokens independently with no attention mask
   • It predicts in sampled factorization orders without inserting a [MASK] input token, using attention masks
   • It physically shuffles the sentence before every prediction
   • It replaces autoregressive prediction with a denoising autoencoder
7. For a token sequence A B C, suppose a sampled factorization order predicts C before A and B. What context is available when predicting C?
   • The tokens A and B must always be provided first
   • Only the next sentence is available
   • No earlier tokens in that sampled order, although C remains at its original input position
   • The input sequence is physically reordered to C A B
8. How does XLNet prevent the target token from leaking into its own prediction query?
   • It removes the target from the entire training sequence
   • It uses the target’s label as an input embedding
   • It replaces the token with a [MASK] symbol
   • The query stream represents the target position without its token content