Kedis ku xidhan hagaha · Fudud Heerka

Kedis kala soocida qoraalka

Tijaabi shaqada summada qoraalka, hawlaha summada badan leh, dheellitirnaan la'aanta, dib-u-celinta, isticmaalka eber-ku-dhufashada ee hawsha gaarka ah, ku-xidhka, xisaabinta saxda ah, iyo buundooyinka soo-saarka.

Wadooyinka haga ee la xidhiidhaKala soocidda qoraalka
Su'aal 1 ee 8

How does a text classifier structure the output for an input document?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. How does a text classifier structure the output for an input document?
    • By necessarily translating it into another language
    • By assigning one or more predefined category labels
    • By generating only a new unrelated paragraph
    • By changing its compressed file format
  2. Xaaladdee ayaa tusaale u ah soocidda summada-badan?
    • Ku calaamadaynta iimaylka sidii spam ama aan spam ahayn
    • Ku-wareejinta tigidhka si sax ah waax
    • Doorashada hal xiddig oo qiimeeya
    • Ku dhejinta maqaal labadaba 'siyaasad' iyo 'dhaqaale' labadaba hal mar
  3. On a text dataset dominated by one class, what can a high overall accuracy conceal?
    • An inability to calculate any metric
    • Proof that recall equals one for every category
    • Failure to identify cases of a rare class
    • A guarantee that every minority example is correct
  4. Maxay dib u xasuusashadu ku cabbirtaa hawsha kala-soocidda?
    • Immisa ka mid ah kiisaska dhabta ah ee nidaamku si guul leh u qabsaday
    • Immisa shay oo calaamadeysan ayaa dhab ahaantii sax ahaa
    • Sida dhakhsaha ah ee moodelku u socdo
    • Intee in le'eg ayaa moodelku yahay
  5. In task-specific zero-shot text classification, what is absent for the new classification task?
    • All model pretraining of any kind
    • Every description of the candidate categories
    • The ability to use any learned representation
    • Labeled training examples for that task
  6. How can learned text embeddings contribute to a classification pipeline?
    • They guarantee perfect predictions on every new domain
    • They provide numerical features used to compute label scores
    • They delete every grammatical distinction by definition
    • They replace all label definitions with filenames
  7. A stated text-classification evaluation has six true positives and two false positives. What is precision for the designated positive class?
    • 75 percent
    • 25 percent
    • 100 percent
    • Six percent
  8. Which output-score design permits several text labels to score highly at once rather than requiring scores to sum to one?
    • A single softmax over mutually exclusive classes
    • A rule requiring exactly one positive label
    • A separate sigmoid output for each label
    • A filename-only representation without label scores