quiz bu lëkkale ak tegtal · Yomb Niveau
Quiz tokenisation sous-baatu
Test kompromis vocabulaire, BERT WordPiece, màrker wéyal, barab bu weex ci piyeesu frase, ID numérique, piyeesu bokk, dagg Unigram, ak bibliotek.
Laaj 1 de 8
Which vocabulary limitation motivates representing rare word forms through smaller reusable pieces?
Xoolaatal laaj bu nekk8 laaj
Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.
- Which vocabulary limitation motivates representing rare word forms through smaller reusable pieces?
- Every smaller piece necessarily has one unique dictionary meaning
- A finite word-only vocabulary cannot allocate an entry to every possible word form
- A subword model requires no numerical input representation
- Whole-word vocabularies cannot contain any common word
- Which subword tokenizer does the original BERT use?
- A word-only alphabetical index
- A mandatory one-byte-per-word lookup
- WordPiece
- An image-pixel segmentation network
- Which prefix marks word-internal continuation pieces in BERT's published WordPiece convention?
- ##
- A required trailing full stop
- A leading plus sign on every whole word
- Capitalization of every suffix
- What SentencePiece input-handling property supports text without explicit whitespace word boundaries?
- It requires every word to have spaces on both sides
- It translates every sentence into English before segmentation
- It deletes all non-Latin characters before training
- It can train from raw sentences without language-specific word pre-tokenization
- Lan la fragment subword bu nekk di mujjee ci karte balaa muy yegg ci model bi?
- Kulòor
- Reseau neuronal bu wuute
- ID lim bu mat
- Benn clip audio
- If related word forms share an available vocabulary piece, what can that sharing support?
- A guarantee that every token is a correct linguistic morpheme
- Reuse of the representation associated with that piece
- Deletion of every differing suffix from the original input
- A requirement that all related words receive the same complete ID sequence
- How does Unigram vocabulary training differ from BPE's bottom-up pair merging?
- It requires a human dictionary definition for every piece
- It only merges the most frequent pair until the corpus is one token
- It permanently removes all character-level coverage regardless of configuration
- It starts with candidate pieces and prunes them according to an objective
- Why should SentencePiece not be treated as another name for the Unigram algorithm?
- It is a library that supports both Unigram and BPE models
- It is the name of BERT's ## continuation marker
- It is a universal ID-to-word table shared by every model
- It only implements whole-word splitting on English spaces