Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Isku-xidhka Hay'adda iyo Kala-duwanaanta

Tijaabadu waxay sheegtaa ogaanshaha, macnaha guud, jiilka musharaxa, soo celinta laba-kumbuyuutar, dib u habeynta codeeyaha, wax soo saarka NIL, iyo isku xirnaanta heerka dukumeentiga.

Wadooyinka haga ee la xidhiidhaIsku xidhka Hay'adda iyo Madmadowga
Su'aal 1 ee 8

Muxuu ku xidhayaa cidda ku xidhaysa xusista qoraalka?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Muxuu ku xidhayaa cidda ku xidhaysa xusista qoraalka?
    • Bog shabakad ah oo random
    • Tababar cusub oo socda
    • sixitaanka higaada
    • Gelid gaar ah oo saldhig aqooneed
  2. Weedha 'shirkadda Apple ayaa soo saartay taleefoon', waa maxay hawsha kala-jajabinta?
    • Go'aaminta Apple macnaheedu waa midhaha, maaha shirkadda
    • Go'aaminta Apple waxay la macno tahay shirkadda tignoolajiyada, ee maaha midhaha
    • U turjumida Apple luqad kale
    • Tirinta xarfaha Apple
  3. Waa maxay ujeedada marxaladda jiilka musharaxiinta?
    • Si aad u qorto maqaal cusub
    • Si loo tirtiro ereyada madmadow
    • Si aad u soo ceshato liis kooban oo ah hay'adaha ku habboon ee suurtogalka ah
    • Si loo turjumo qoraalka
  4. Door noocee ah ayuu ka ciyaaraa bi-encoder ee isku xirka cidda casriga ah?
    • Soo celinta degdega ah iyada oo la adeegsanayo qalabyo kala duwan oo lagu xuso iyo hay'adaha
    • Calaamadaynta buug-gacmeedka oo gaabis ah oo tilmaame kasta ah
    • Soo saarista sawirada qaybaha
    • Sirin sirta saldhigga aqoonta
  5. Muxuu fasalka NIL ka dhigan yahay isku xirka cidda?
    • Kooxda ugu caansan
    • Xusid aan lahayn galitaanka aqoonta-saldhigga ku habboon
    • Koox nuqul ah
    • Qalad tababar
  6. How can document-level coherence help an entity-linking system?
    • By guaranteeing that every mention has the same entity identifier
    • By proving every document discusses only one topic
    • By removing the need to evaluate individual mention contexts
    • By considering whether candidate assignments fit the document context and one another
  7. After fast candidate retrieval, what information does a cross-encoder jointly read for entity-linking reranking?
    • The mention's context and a candidate entity's description
    • Only the entity's numeric identifier with no text
    • Only the two input filenames
    • Only the number of images in the knowledge base
  8. If an entity-linking pipeline never proposes the span 'Apple' for analysis, which initial stage has missed that mention?
    • The final candidate reranker only
    • The image-compression stage
    • Mention detection
    • The knowledge base's output filename formatter