[Solved] What will be the output of this code? from gensim.models import FastText sentences = [['cat', 'dog'], ['dog', 'mouse']] model = FastText(sentences,... What will be the output of this code? | NLP

NLP - Word Embeddings

What will be the output of this code?

from gensim.models import FastText
sentences = [['cat', 'dog'], ['dog', 'mouse']]
model = FastText(sentences, vector_size=5, window=2, min_count=1, epochs=3)
vector1 = model.wv['cat']
vector2 = model.wv['dog']
import numpy as np
print(np.dot(vector1, vector2))

AError: 'cat' not in vocabulary

Bnull

CA vector of length 5

DA float number representing similarity

Step-by-Step Solution

Solution:

Step 1: Understand dot product of word vectors
Dot product between two vectors returns a single float number representing similarity.
Step 2: Check vocabulary and vector retrieval
Both 'cat' and 'dog' are in the training sentences, so vectors exist and dot product is valid.
Final Answer:
A float number representing similarity -> Option D
Quick Check:
Dot product output = float number = B [OK]

Quick Trick: Dot product of vectors returns a single float [OK]

Common Mistakes:

MISTAKES

Expecting vector output from dot product
Assuming word missing from vocabulary
Confusing dot product with concatenation

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What will be the output of this code?

Step 1: Understand dot product of word vectors

Step 2: Check vocabulary and vector retrieval

Final Answer:

Quick Check:

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