quiz bu lëkkale ak tegtal · Diggante Niveau

Matryoshka

Saytu ni tàggat Matryoshka di defee prefix yu am njariñ ci dimension yu bari te jàppale tànneefi dencukaay ak seetlu.

Yooni njiit yu jëm ciMatryoshka
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Lan mooy màndarga gi gëna am solo ci Matryoshka?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Lan mooy màndarga gi gëna am solo ci Matryoshka?
    • Trained prefixes can serve as representations at multiple lengths
    • Bépp dimension amna solo bu tolloo te yoon amul benn njariñ
    • Mën nañu ko jëfandikoo ci benn guddaay buñ tëral
    • Dañu ko wara kodewaat ngir dayo biñ bëgga
  2. naka lañuy tàggatee xeetu Matryoshka ngir mëna def loolu?
    • Ni ñuy tàggatee xeetu model bu nekk ngir dayo bu nekk
    • Ak perte buñ boole ci dimension prefix yu bari yuñ boole
    • Ni ñuy jëfandikoo PCA ginaaw tàggat buñ miin
    • Dañuy wàññi dimension yi ci waxtu test bi kese
  3. Lan ngay def ci inference ngir am embedding bu gëna ndaw?
    • Taggaat model bi ci noonu
    • Doxal ab reso kompresioŋ bu wuute
    • Dagg ci dimension k yi njëkk te normalise ko
    • Moyenne dimension yépp boole leen
  4. Which documented API lets a caller request fewer dimensions for text-embedding-3 output?
    • The dimensions parameter
    • A PCA flag in the Matryoshka paper
    • A FastText n-gram option
    • A GloVe context-window setting
  5. Lan mooy njariñ li gëna am solo ci dugal Matryoshka?
    • Fewer coordinates may reduce storage and comparison work; benchmark retrieval quality and cost
    • Dina ñu baña soxla benn vecteur
    • Dañuy garanti njubte bu mat sëkk ci bépp dayo
    • Nataal yi kese lañuy liggéey
  6. Ci biir benn Matryoshka, fan la xibaar bi gëna tàqale nit ñi dajaloo?
    • Ci njeexte li, dimension yi ci topp
    • Ci dimension yi njëkka am
    • Tasaaroo ci anam wu maase te amul benn komànd
    • Ci diggu dimension yi kese
  7. Which dimensionality schedule is an example of nested Matryoshka prefixes in the guide?
    • Only one fixed size of 512 dimensions
    • A decreasing schedule from 2048 to 1 dimension with separate models
    • 8, 16, 32, and progressively larger prefixes up to 2048 dimensions
    • A sequence of unrelated sizes requiring new model weights each time
  8. How does truncating a Matryoshka embedding differ from applying a separate PCA model?
    • It requires retraining a different encoder for every dimension
    • It replaces the original vector with a random projection
    • It can only be done while the model is being trained
    • A shorter prefix can be sliced and renormalized from the same trained embedding without a separate projection model