Word2Vec (CBOW and Skip-gram) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Word2Vec model from gensim.

NLP

from gensim.models import [1]

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

AWord2Vec

BFastText

CDoc2Vec

DLdaModel

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated models like FastText or Doc2Vec instead of Word2Vec.

The Word2Vec class is imported from gensim.models to create word embedding models.

2fill in blank

medium

Complete the code to initialize a CBOW Word2Vec model with vector size 100.

NLP

model = Word2Vec(sentences, vector_size=[1], window=5, sg=0, min_count=1)

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

A50

B200

C100

D300

Attempts:

3 left

💡 Hint

Common Mistakes

Using vector_size values that are too small or too large without reason.

The vector_size parameter sets the size of the word vectors. 100 is a common choice.

3fill in blank

hard

Fix the error in the code to train a Skip-gram Word2Vec model.

NLP

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

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

A0

B1

C2

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using sg=0 which trains CBOW instead of Skip-gram.

Using invalid values like 2 or -1 for sg.

Setting sg=1 configures the model to use the Skip-gram architecture.

4fill in blank

hard

Fill both blanks to create a dictionary of word vectors for words with frequency above 2.

NLP

word_vectors = {word: model.wv[[1]] for word in model.wv.index_to_key if model.wv.get_vecattr(word, '[2]') > 2}

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

Aword

Bcount

Cfrequency

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'frequency' instead of 'count' for the attribute name.

Using 'index' instead of the actual word to get vectors.

We use 'word' to get the vector and 'count' to check the frequency attribute.

5fill in blank

hard

Fill all three blanks to find the top 3 most similar words to 'king'.

NLP

similar_words = model.wv.most_similar(positive=[[1]], topn=[2])
result = [word for word, [3] in similar_words]

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

A'king'

B3

Csimilarity

D'queen'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable name instead of 'similarity' in the unpacking.

Passing a word not in quotes to the positive list.

We search for words similar to 'king', get top 3, and extract words ignoring similarity scores.

Practice

1. What is the main difference between the CBOW and Skip-gram models in Word2Vec?

easy

A. CBOW uses one-hot encoding, Skip-gram uses frequency encoding.

B. CBOW predicts a word based on its context, while Skip-gram predicts context words from a target word.

C. CBOW is used only for sentences, Skip-gram only for paragraphs.

D. CBOW requires labeled data, Skip-gram does not.

Solution

1. Step 1: Understand CBOW model purpose

   CBOW tries to predict the target word using the surrounding context words.

2. Step 2: Understand Skip-gram model purpose

   Skip-gram tries to predict the surrounding context words given the target word.

3. Final Answer:

   CBOW predicts a word based on its context, while Skip-gram predicts context words from a target word. -> Option B

4. Quick Check:

   CBOW = context to word, Skip-gram = word to context [OK]

Hint: Remember CBOW = context to word, Skip-gram = word to context [OK]

Common Mistakes:

• Confusing which model predicts context vs. target word
• Thinking both models do the same prediction
• Assuming CBOW needs labeled data

2. Which of the following is the correct way to initialize a Skip-gram Word2Vec model using the Gensim library in Python?

easy

A. Word2Vec(sentences, size=100, window=5, sg=0)

B. Word2Vec(sentences, vector_size=100, window=5, sg=0)

C. Word2Vec(sentences, size=100, window=5, sg=1)

D. Word2Vec(sentences, vector_size=100, window=5, sg=1)

Solution

1. Step 1: Identify correct parameter for Skip-gram

   In Gensim, 'sg=1' sets Skip-gram, 'sg=0' sets CBOW.

2. Step 2: Use correct parameter names

   Since Gensim 4.0+, 'vector_size' replaces 'size' for embedding dimension.

3. Final Answer:

   Word2Vec(sentences, vector_size=100, window=5, sg=1) -> Option D

4. Quick Check:

   sg=1 and vector_size used correctly [OK]

Hint: Use sg=1 for Skip-gram and vector_size for embedding size [OK]

Common Mistakes:

• Using 'size' instead of 'vector_size' in recent Gensim versions
• Setting sg=0 which is CBOW, not Skip-gram
• Confusing sg parameter values

3. Given the following code snippet using Gensim's Word2Vec with Skip-gram, what will be the output of model.wv.most_similar('king', topn=1) if the model is trained on a typical English corpus?

medium

A. [('run', similarity_score)]

B. [('apple', similarity_score)]

C. [('queen', similarity_score)]

D. [('car', similarity_score)]

Solution

1. Step 1: Understand Word2Vec similarity

   Word2Vec finds words with similar meanings or contexts; 'queen' is semantically close to 'king'.

2. Step 2: Analyze typical English corpus relations

   Words like 'apple', 'car', or 'run' are unrelated to 'king' in meaning or context.

3. Final Answer:

   [('queen', similarity_score)] -> Option C

4. Quick Check:

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

Hint: Most similar to 'king' is usually 'queen' in English corpora [OK]

Common Mistakes:

• Choosing unrelated words as most similar
• Confusing syntactic similarity with semantic similarity
• Expecting exact similarity scores

4. You trained a CBOW Word2Vec model but get an error: KeyError: 'unknown_word' when querying model.wv['unknown_word']. What is the most likely cause and fix?

medium

A. The word was not in training data; retrain with larger corpus or check vocabulary before querying.

B. The model was trained with Skip-gram; switch to CBOW to fix.

C. The vector size is too small; increase vector_size parameter.

D. The window size is too large; reduce window parameter.

Solution

1. Step 1: Understand KeyError cause

   KeyError occurs when the queried word is not in the model's vocabulary.

2. Step 2: Fix by ensuring word presence

   Either add the word to training data or check if word exists before querying to avoid error.

3. Final Answer:

   The word was not in training data; retrain with larger corpus or check vocabulary before querying. -> Option A

4. Quick Check:

   KeyError means word missing in vocabulary [OK]

Hint: Check if word is in vocabulary before querying model vectors [OK]

Common Mistakes:

• Assuming model type (CBOW/Skip-gram) causes KeyError
• Changing vector or window size to fix missing word error
• Ignoring vocabulary check before querying

5. You want to train a Word2Vec model to capture rare word meanings better. Which approach is best?

hard

A. Use Skip-gram with a smaller window size and increase training epochs.

B. Use CBOW with a large window size and fewer epochs.

C. Use Skip-gram with a large window size and fewer epochs.

D. Use CBOW with a smaller window size and increase training epochs.

Solution

1. Step 1: Identify model for rare words

   Skip-gram is better at learning rare word representations than CBOW.

2. Step 2: Adjust window size and epochs

   Smaller window focuses on close context, improving rare word meaning; more epochs improve training quality.

3. Final Answer:

   Use Skip-gram with a smaller window size and increase training epochs. -> Option A

4. Quick Check:

   Skip-gram + small window + more epochs = better rare word capture [OK]

Hint: Skip-gram + small window + more epochs helps rare words [OK]

Common Mistakes:

• Choosing CBOW for rare word learning
• Using large window size which dilutes context
• Reducing epochs which limits training