quiz bu lëkkale ak tegtal · Diggante Niveau

Quiz buy boole santaane-BERT

Saytu naka la samp ay frase di jàppale seetlu semantik buñ mëna escaleer, boole ci misaali Matryoshka ak rang encoder yu wuute.

Yooni njiit yu jëm ciBert santaane
Laaj 1 de 8

Ban jafe-jafe bu mag la Sentence-BERT di saafara ci BERT bu leer bi?

Xoolaatal laaj bu nekk8 laaj

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

  1. Ban jafe-jafe bu mag la Sentence-BERT di saafara ci BERT bu leer bi?
    • Teg santaane yu bari ci ñaar-ñaar ak BERT lu yeex la
    • BERT mënul lire lu ëpp benn frase
    • BERT ci Àngle kese lay liggéey
    • Mënuñu defar BERT
  2. Ban jumtukaayu reso la Sentence-BERT di jëfandikoo?
    • Benn dekodeer buy dellu ginaaw
    • Reseau siamese bu am ñaari tour BERT yuy séddoo diisaay
    • ab reso nataal buy jaxasoo
    • Agent jàng buy dooleel
  3. Which pooling strategy did the original Sentence-BERT paper evaluate for creating sentence embeddings?
    • Max pooling ci kaw boppu yi
    • Jëfandikool arafu njëkk bi kese
    • pooling moyenne ci kaw token yiñ samp
    • Tanneef bu bari ci benn jeton
  4. Buñu boole ay frase, naka lañuy nattee seen nuru?
    • Dawal BERT ci ñoom ñaar
    • Lim araf yiñ bokk
    • Tegtale dayo fichier yi
    • Nuru cosinus (produit point) ci digganté vecteur
  5. What training data did the original Sentence-BERT paper use for NLI fine-tuning?
    • Natural Language Inference examples labeled for entailment, contradiction and neutral relations
    • Only satellite images with land-cover labels
    • Only audio recordings with speaker labels
    • Only source code and bug reports
  6. In a possible semantic-retrieval workflow, what can a Sentence-BERT bi-encoder do?
    • Cross-attend to every query-candidate pair before any encoding
    • Encode a query and candidate texts separately so vector similarity can rank candidates
    • Guarantee retrieval quality for every corpus
    • Replace the need to evaluate relevance
  7. What does Matryoshka Representation Learning enable for an embedding?
    • It guarantees identical quality at every vector size
    • It converts embeddings into labeled text tokens
    • A single representation can be truncated to different dimensions to trade storage or compute for task performance
    • It removes the need for similarity comparisons
  8. In a common hybrid retrieval pipeline, what happens after a bi-encoder retrieves candidate passages?
    • The candidate text is converted into audio
    • The bi-encoder is discarded and no ranking occurs
    • The system averages all documents into one sentence
    • A cross-encoder can score query-passage pairs to rerank the candidates