Given the following Python code using Gensim FastText model:

medium📝 Predict Output Q13 of 15

NLP - Word Embeddings

from gensim.models import FastText
sentences = [['cat', 'sat', 'on', 'mat'], ['dog', 'barked']]
model = FastText(sentences, vector_size=10, window=3, min_count=1, epochs=5)
print(model.wv['cat'])

What will be the output type of model.wv['cat']?

AA numpy array representing the vector embedding of 'cat'

BAn integer representing the frequency of 'cat'

CA list of words similar to 'cat'

DA string with the word 'cat'

Step-by-Step Solution

Solution:

Step 1: Understand what model.wv['word'] returns in Gensim FastText
model.wv['cat'] returns the vector embedding as a numpy array representing the word 'cat'.
Step 2: Check other options for output type
A list of words similar to 'cat' is for similar words, not the vector. An integer representing the frequency of 'cat' is frequency, which is not returned here. A string with the word 'cat' is just the word string, not the vector.
Final Answer:
A numpy array representing the vector embedding of 'cat' -> Option A
Quick Check:
model.wv['word'] returns vector array [OK]

Quick Trick: model.wv['word'] gives vector array, not word list [OK]

Common Mistakes:

MISTAKES

Expecting a list of similar words instead of vector
Thinking it returns frequency count
Confusing word string with vector

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More NLP Quizzes

Given the following Python code using Gensim FastText model:

Step 1: Understand what model.wv['word'] returns in Gensim FastText

Step 2: Check other options for output type

Final Answer:

Quick Check:

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