Kediska Ku-xidhka Erayada

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. How does a learned word embedding represent a word for numerical comparison?
   • As a plain-text dictionary entry only
   • As a vector of learned numerical features
   • As a complete recorded pronunciation only
   • As a manually assigned grammar rule
2. Sidee bay moodooyinka sida word2vec u bartaan macnaha erayga?
   • Adigoo akhrinaya qeexitaan gacan-ku-qoran oo qaamuus ah
   • Adigoo ku tirinaya xarfaha kelmad kasta
   • Adigoo eegaya erayada kale ee u janjeera inay agtiisa ka soo baxaan oo tiro badan oo qoraal ah
   • Adiga oo waydiinaya qiimeeyayaasha bini'aadamka inay dhaliyaan kelmad kasta
3. What does the classic king-minus-man-plus-woman vector example illustrate?
   • Some learned representations capture relationships through vector offsets
   • Every word relation obeys an exact arithmetic law
   • Vector arithmetic verifies every factual statement
   • The vector stores a word's exact spelling only
4. Waa maxay faraqa ugu weyn ee udhaxeeya word2vec/GloVe galinta iyo ku dhejinta gudaha moodooyinka casriga casriga ah?
   • Transformers ma isticmaalaan tirooyin gabi ahaanba
   • GloVe kaliya waxay ku shaqeysaa sawirada
   • Moodooyinka casriga ahi ma matali karaan macnaha
   • Word2vec waxa uu bixiyaa hal erey oo go'an erey kasta, halka transformers-yadu ay soo saaraan vector-ku-tiirsanaan guud
5. Hawshee sida tooska ah ugu tiirsan tahay is-barbardhigga vectors-ka?
   • Tirinta wadarta tirada erayada dukumeentiga
   • U beddelashada qoraalka far waaweyn
   • Raadinta semantic ee hela dukumentiyada macne la mid ah weydiinta
   • Ku hubinta cabbirka faylka diskka
6. Which scale of dimensionality is found in common released classic word-vector configurations?
   • Exactly one coordinate for every model
   • Tens or hundreds of learned coordinates per word
   • Exactly two coordinates in all word2vec models
   • At least ten million coordinates per word by definition
7. In a toy word-vector comparison, what is the cosine similarity of the nonzero vectors one-zero and zero-one?
   • Mid
   • Minus one
   • Laba
   • Eber
8. What should the first coordinate of an arbitrary learned word embedding be assumed to mean?
   • A learned feature without a universal predetermined linguistic label
   • The word's character count in every model
   • A fixed sentiment score in every dataset
   • The document's factual reliability by definition