Kediska Weedhaha-BERT Cutubka

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay dhibatada udub-dhexaadka u ah BERT ee cad ee uu xalliyo jumlada-BERT?
   • Isbarbardhigga weedho badan si wadajir ah iyo BERT aad ayey u gaabis u tahay
   • BERT ma akhrin karo wax ka badan hal jumlad
   • BERT waxay ku shaqeysaa Ingiriis kaliya
   • BERT si fiican looma hagaajin karo
2. Waa maxay qaab-dhismeedka shabakadeed ee uu isticmaalo Sentence-BERT?
   • Cod-dejiye keli ah oo iskiis ah
   • Shabakad siamese ah oo leh laba munaaradood oo BERT ah oo wadaaga miisaanka
   • Shabakad muuqaal is-daba-joog ah
   • Wakiilka xoojinta-barashada
3. Istaraatiijiyadda isku-darka ee warqadda asalka ah ee Sentence-BERT ay qiimeysay si loo abuuro embedding-yada jumladaha?
   • Isku ururinta ugu badan ee madaxa dareenka
   • Isticmaalka kaliya jilaha ugu horreeya
   • Macnaheedu waa isku-dubbarid ka sarreeya gundhigga calaamadda
   • Xulashada random ee hal calaamad
4. Marka weedhaha la dhex dhigo, sidee isku mid ahaanshahooda loo cabbiraa?
   • Ku socodsiinta BERT mar labaad labadaba
   • Tirinta jilayaasha la wadaago
   • Isbarbardhigga cabbirrada faylka
   • Isku ekaanshaha cosine (wax soo saarka dhibicda) ee u dhexeeya vectors
5. Waa maxay xogta tababarka ee warqadda asalka ah ee Sentence-BERT ay u isticmaashay si ay u sameyso 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