Word embeddings concept (Word2Vec) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Word2Vec model from gensim.

ML Python

from gensim.models import [1]

Drag options to blanks, or click blank then click option'

AWord2Vec

Bword2vec

CWordVec

DWordEmbedding

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'word2vec' which is incorrect.

Using 'WordVec' missing the '2'.

Using 'WordEmbedding' which is not the class name.

The correct class name to import is 'Word2Vec' with capital W and V.

2fill in blank

medium

Complete the code to train a Word2Vec model on sentences.

ML Python

model = Word2Vec(sentences=[1], vector_size=100, window=5, min_count=1, workers=4)

Drag options to blanks, or click blank then click option'

Asentences

Bdata

Ccorpus

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sentences' as a variable when it is not defined.

Using 'text' which is usually raw string, not tokenized sentences.

Using 'data' which is too generic and may not be defined.

The training data variable is commonly called 'corpus' representing the list of sentences.

3fill in blank

hard

Fix the error in accessing the vector for the word 'king'.

ML Python

vector = model.wv[[1]]

Drag options to blanks, or click blank then click option'

A'King'

Bking

C['king']

D'king'

Attempts:

3 left

💡 Hint

Common Mistakes

Using capitalized 'King' which may not be in vocabulary.

Using the variable king without quotes causing NameError.

Using a list ['king'] which is invalid for key access.

The word must be a lowercase string 'king' to match the vocabulary key.

4fill in blank

hard

Fill both blanks to find the most similar words to 'queen'.

ML Python

similar_words = model.wv.[1](positive=[[2]], topn=5)

Drag options to blanks, or click blank then click option'

Amost_similar

B'queen'

C'king'

Dsimilar_words

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'similar_words' as method which is incorrect.

Passing 'king' instead of 'queen' as the positive word.

Not using quotes around the word.

The method to find similar words is 'most_similar' and the input word must be a string 'queen'.

5fill in blank

hard

Fill both blanks to create a dictionary of word vectors for words with length greater than 4.

ML Python

word_vectors = {word: model.wv[[1]] for word in model.wv.index_to_key if len(word) [2] 4}

Drag options to blanks, or click blank then click option'

Aword

B>

C<

Dlen(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'len(word)' as dictionary key which is incorrect.

Using '<' instead of '>' causing wrong filtering.

Using variable names not defined in the comprehension.

We use 'word' as the key to get vectors, and filter words with length greater than 4 using '>'.

Practice

1. What is the main purpose of Word2Vec in machine learning?

easy

A. To translate text from one language to another

B. To count the number of words in a sentence

C. To convert words into numerical vectors that capture their meanings

D. To generate random words for testing

Solution

1. Step 1: Understand Word2Vec's role

   Word2Vec creates vectors representing words based on their context, capturing meaning.

2. Step 2: Compare options

   Only To convert words into numerical vectors that capture their meanings describes converting words into meaningful numerical vectors.

3. Final Answer:

   To convert words into numerical vectors that capture their meanings -> Option C

4. Quick Check:

   Word2Vec = word vectors [OK]

Hint: Word2Vec turns words into numbers showing meaning [OK]

Common Mistakes:

• Thinking Word2Vec translates languages
• Confusing Word2Vec with word counting
• Assuming Word2Vec generates random words

2. Which of the following is the correct way to import Word2Vec from the gensim library in Python?

easy

A. import gensim.Word2Vec

B. import Word2Vec from gensim.models

C. from gensim import Word2Vec

D. from gensim.models import Word2Vec

Solution

1. Step 1: Recall correct Python import syntax

   Python uses 'from module import class' to import specific classes.

2. Step 2: Match with gensim syntax

   The correct import is 'from gensim.models import Word2Vec'. Other options have wrong syntax.

3. Final Answer:

   from gensim.models import Word2Vec -> Option D

4. Quick Check:

   Correct import syntax = from gensim.models import Word2Vec [OK]

Hint: Use 'from module import class' for specific imports [OK]

Common Mistakes:

• Using 'import Word2Vec from ...' which is invalid syntax
• Trying to import directly from 'gensim' without 'models'
• Using dot notation in import statements incorrectly

3. Given the following code snippet, what will be the output of print(model.wv.most_similar('king', topn=1))?

from gensim.models import Word2Vec
sentences = [['king', 'queen', 'man', 'woman'], ['prince', 'princess', 'boy', 'girl']]
model = Word2Vec(sentences, vector_size=10, window=2, min_count=1, epochs=10)

medium

A. [('man', similarity_score)] where similarity_score is close to 0

B. [('queen', similarity_score)] where similarity_score is close to 1

C. Error because 'king' is not in vocabulary

D. Empty list []

Solution

1. Step 1: Check if 'king' is in vocabulary

   'king' appears in the sentences, so it is in the model's vocabulary.

2. Step 2: Understand most_similar output

   Word2Vec finds words with similar context. 'queen' is closely related to 'king' in the data, so it will have highest similarity.

3. Final Answer:

   [('queen', similarity_score)] where similarity_score is close to 1 -> Option B

4. Quick Check:

   Most similar to 'king' = 'queen' [OK]

Hint: Most similar words share context, 'queen' pairs with 'king' [OK]

Common Mistakes:

• Assuming 'king' is missing from vocabulary
• Expecting unrelated words like 'man' as most similar
• Thinking output is an error or empty list

4. Identify the error in this Word2Vec training code and select the fix:

from gensim.models import Word2Vec
sentences = [['cat', 'dog', 'mouse'], ['dog', 'cat', 'elephant']]
model = Word2Vec(sentences, vector_size=5, window=2, min_count=2, epochs=5)
model.train(sentences, total_examples=1, epochs=5)

medium

A. Set min_count=1 to include all words in training

B. Change total_examples to len(sentences) in train()

C. Remove the train() call because Word2Vec trains on initialization

D. Increase vector_size to at least 10

Solution

1. Step 1: Analyze min_count effect

   min_count=2 excludes words appearing less than twice. 'mouse' and 'elephant' appear once, so they are ignored.

2. Step 2: Fix by lowering min_count

   Setting min_count=1 includes all words, fixing vocabulary size and training coverage.

3. Final Answer:

   Set min_count=1 to include all words in training -> Option A

4. Quick Check:

   min_count controls word inclusion [OK]

Hint: min_count filters words; lower it to include rare words [OK]

Common Mistakes:

• Ignoring min_count effect on vocabulary
• Thinking train() is unnecessary or wrongly used
• Changing vector_size without addressing vocabulary issue

5. You want to find the word that best fits the analogy: 'king' is to 'man' as 'queen' is to ____. Using Word2Vec, which operation would you perform to find this word?

hard

A. vector('king') - vector('man') + vector('queen')

B. vector('king') + vector('man') - vector('queen')

C. vector('queen') - vector('king') + vector('man')

D. vector('man') - vector('king') + vector('queen')

Solution

1. Step 1: Understand analogy vector math

   Word2Vec analogies use vector arithmetic: 'king' - 'man' + 'queen' approximates the vector for the missing word.

2. Step 2: Apply formula to find the answer

   Performing vector('king') - vector('man') + vector('queen') finds the word similar to 'queen' but related like 'king' is to 'man'.

3. Final Answer:

   vector('king') - vector('man') + vector('queen') -> Option A

4. Quick Check:

   Analogy = king - man + queen [OK]

Hint: Use king - man + queen to solve analogies [OK]

Common Mistakes:

• Mixing up vector addition and subtraction order
• Using unrelated vector combinations
• Ignoring analogy vector math concept