Quiz ngir dugal ay sous-baatu FastText

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

1. naka la FastText di màndargaale benn baat?
   • Ni sac bu def n-gram yuñ boole
   • Benn token buñu mëna xaajale
   • Ni muy tekki ci diksoneer
   • Ni vecteur aleatoire
2. Ban jafe-jafe bu mag la FastText mëna daan ci Word2Vec ak GloVe?
   • Dañuy daw xél
   • Mënu ñu sos vecteur ngir baat yi nekk ci bitti vocabulaire
   • Duñu bàyyi xel ci ponksioŋ yi
   • Angale rek lañuy liggéey
3. Ngir baat "fan" ak n-gram yu am 3 araf, ban n-gram mooy baax (ak ay màrker ci pegg yi)?
   • <fu>rekk
   • xyz
   • fu
   • bi
4. Ban mébetu tàggat yaram la modelu embedding bu FastText tabax ci kaw?
   • Faktorisasioŋ matriks GloVe
   • Tekki làkk ci wàllu frase
   • Ñàkka xaaj nataal
   • Skip-Gram ak échantillonnage négatif
5. Lan moo tax FastText am njariñ ci làkk yu melni Finlande wala Turc?
   • Amna ñu bari ci wàllu morphologie ak xeeti baat yu bari yuñ boole ay sous-baat
   • Am nañu kàddu yu néew lool
   • Jëfandikoo wuñu ay bayaal
   • Amu ñu kàddu yu bari
6. Which separate method did Sennrich and colleagues describe for representing rare words in neural machine translation?
   • Byte-pair encoding as subword units
   • FastText’s hashed character n-gram bucket table
   • A full-word-only vocabulary that discards rare tokens
   • GloVe’s global co-occurrence matrix
7. How does FastText store learnable features for its character n-grams?
   • It stores every complete sentence as a separate vector
   • It assigns each n-gram a pixel grid in a convolutional decoder
   • It hashes n-grams into a fixed-size bucket table with learned vectors
   • It replaces all subword features with a hand-written dictionary
8. What can fastText's supervised mode do efficiently with its bag-of-features model?
   • Estimate the 3D shape of an object from a single image
   • Generate new audio waveforms from a spectrogram
   • Compute a global word-word co-occurrence matrix only
   • Classify text for tasks such as language identification or tagging