FAQ: Word Embeddings - Review

This community-built FAQ covers the “Review” exercise from the lesson “Word Embeddings”.

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This exercise can be found in the following Codecademy content:

Natural Language Processing

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I run code on VS Code the result is different than running here in codecademy.

  • codecademy result :
    0.22602969408035278 (sponge_vec, startfish_vec)
    0.5690327882766724 (sponge_vec, squid_vec)
    0.27446436882019043 (starfish_vec, squid_vec)

  • VS Code result :
    0.8279428035020828 (sponge_vec, starfish_vec)
    0.6669286489486694 (sponge_vec, squid_vec)
    0.8470767140388489 (starfish_vec, squid_vec)

the code is like this

import spacy
from scipy.spatial.distance import cosine

# load word embadding model
nlp = spacy.load('en_core_web_sm')

# define word embadding vectors
sponge_vec = nlp('sponge').vector
starfish_vec = nlp('starfish').vector
squid_vec = nlp('squid').vector

# compare vector with cosine distance
dist_sponge_star = cosine(sponge_vec, starfish_vec)
dist_sponge_squid = cosine(sponge_vec, squid_vec)
dist_star_squid = cosine(starfish_vec, squid_vec)

print(dist_sponge_star)
print(dist_sponge_squid)
print(dist_star_squid)
the code differences
nlp = spacy.load('en_core_web_sm') - VS Code
nlp = spacy.load('en') - codecademy

because I can’t load spacy.load(‘en’) in VS code.

why the results are different?