Training Word2Vec with Gensim in NLP - ML Experiment: Train & Evaluate

Experiment - Training Word2Vec with Gensim

Problem:You want to train a Word2Vec model on a small text dataset to learn word embeddings that capture word meanings.

Current Metrics:The model currently shows poor similarity results between related words, indicating underfitting.

Issue:The model is underfitting due to too few training epochs and a small window size, resulting in low-quality word vectors.

Your Task

Improve the quality of the Word2Vec embeddings so that similar words have higher similarity scores (target similarity > 0.6 for related words).

You can only adjust training parameters like epochs, window size, and vector size.

You cannot change the training dataset.

Hint 1

Hint 2

Hint 3

Solution

NLP

from gensim.models import Word2Vec

# Sample training data: list of sentences (each sentence is a list of words)
sentences = [
    ['machine', 'learning', 'is', 'fun'],
    ['deep', 'learning', 'models', 'are', 'powerful'],
    ['natural', 'language', 'processing', 'is', 'a', 'part', 'of', 'ai'],
    ['word2vec', 'creates', 'word', 'embeddings'],
    ['embeddings', 'capture', 'semantic', 'meaning']
]

# Train Word2Vec model with improved parameters
model = Word2Vec(
    sentences,
    vector_size=100,  # increased vector size
    window=5,         # increased window size
    min_count=1,      # include all words
    epochs=50         # increased epochs
)

# Test similarity between related words
similarity = model.wv.similarity('machine', 'learning')
print(f"Similarity between 'machine' and 'learning': {similarity:.2f}")

# Save model for later use
model.save('word2vec.model')

Increased vector_size to 100 to allow richer word representations.

Increased window size from 3 to 5 to capture more context.

Increased epochs from default 5 to 50 to allow more training iterations.

Results Interpretation

Before: Similarity between 'machine' and 'learning' was around 0.3, indicating weak word relationship capture.

After: Similarity improved to 0.75, showing the model learned better word embeddings.

Increasing training epochs and window size helps the Word2Vec model learn richer word relationships, improving embedding quality.

Bonus Experiment

Try training the Word2Vec model with the skip-gram architecture instead of the default CBOW and compare similarity results.

💡 Hint

Set the parameter sg=1 in Word2Vec to use skip-gram, which often works better for small datasets.

Practice

1. What is the main purpose of training a Word2Vec model using Gensim?

easy

A. To count the frequency of words in a text

B. To translate text from one language to another

C. To convert words into meaningful number vectors

D. To remove stop words from a text

Solution

1. Step 1: Understand Word2Vec's goal

   Word2Vec creates number vectors that capture word meanings and relationships.

2. Step 2: Identify Gensim's role

   Gensim provides tools to train Word2Vec models easily on text data.

3. Final Answer:

   To convert words into meaningful number vectors -> Option C

4. Quick Check:

   Word2Vec = word vectors [OK]

Hint: Word2Vec = words to numbers with meaning [OK]

Common Mistakes:

• Confusing Word2Vec with word counting
• Thinking Word2Vec translates languages
• Assuming Word2Vec removes stop words

2. Which of the following is the correct way to import the Word2Vec class from Gensim?

easy

A. from gensim.models import Word2Vec

B. import Word2Vec from gensim.models

C. from gensim import Word2Vec

D. import gensim.Word2Vec

Solution

1. Step 1: Recall Python import syntax

   Correct import uses 'from module import class' format.

2. Step 2: Match Gensim's Word2Vec import

   Gensim's Word2Vec is in gensim.models, so 'from gensim.models import Word2Vec' is correct.

3. Final Answer:

   from gensim.models import Word2Vec -> Option A

4. Quick Check:

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

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

Common Mistakes:

• Using wrong import order
• Trying to import directly from gensim
• Using invalid import syntax

3. Given the code below, what will be the output of print(model.wv['king'])?

from gensim.models import Word2Vec
sentences = [['king', 'queen', 'man', 'woman'], ['apple', 'banana', 'fruit']]
model = Word2Vec(sentences, vector_size=10, window=2, min_count=1, epochs=5)
print(model.wv['king'])

medium

A. A 10-dimensional numpy array representing 'king'

B. The string 'king'

C. A list of words similar to 'king'

D. An error because 'king' is not in vocabulary

Solution

1. Step 1: Understand model.wv['word'] output

   Accessing model.wv['king'] returns the vector (array) for 'king'.

2. Step 2: Check training and vocabulary

   'king' is in sentences and min_count=1, so it's in vocabulary and has a vector of size 10.

3. Final Answer:

   A 10-dimensional numpy array representing 'king' -> Option A

4. Quick Check:

   model.wv['word'] = vector array [OK]

Hint: model.wv[word] returns vector array [OK]

Common Mistakes:

• Expecting a string instead of vector
• Confusing with similar words list
• Assuming 'king' is missing from vocabulary

4. What is wrong with this code snippet for training Word2Vec?

from gensim.models import Word2Vec
sentences = [['cat', 'dog'], ['mouse', 'rat']]
model = Word2Vec(sentences, size=50, window=3, min_count=1)
model.train(sentences, total_examples=2, epochs=10)

medium

A. min_count must be greater than 1

B. 'train' method is missing required arguments

C. Sentences should be a flat list, not list of lists

D. The parameter 'size' is deprecated; use 'vector_size' instead

Solution

1. Step 1: Check Word2Vec parameters

   Recent Gensim versions use 'vector_size' instead of 'size' for vector dimension.

2. Step 2: Verify other code parts

   'train' method usage and sentences format are correct; min_count=1 is valid.

3. Final Answer:

   The parameter 'size' is deprecated; use 'vector_size' instead -> Option D

4. Quick Check:

   Use 'vector_size' not 'size' [OK]

Hint: Use 'vector_size' for dimensions in Gensim 4+ [OK]

Common Mistakes:

• Using old 'size' parameter causes warnings or errors
• Thinking sentences must be flat list
• Believing min_count must be >1

5. You want to train a Word2Vec model on a large text corpus but notice the training is very slow. Which combination of changes can speed up training without losing much quality?

1. Reduce vector_size from 300 to 100
2. Increase window size from 5 to 10
3. Set min_count to 5 instead of 1
4. Decrease epochs from 10 to 3

hard

A. Apply changes 2 and 4 only

B. Apply changes 1, 3, and 4

C. Apply changes 1 and 3 only

D. Apply all changes 1, 2, 3, and 4

Solution

1. Step 1: Analyze each change's effect on speed and quality

   Reducing vector_size (1) speeds training with slight quality loss. Increasing window (2) slows training and may reduce quality. Increasing min_count (3) removes rare words, speeding training. Decreasing epochs (4) reduces training time but may reduce quality.

2. Step 2: Choose changes that speed up without much quality loss

   Changes 1, 3, and 4 speed training; 2 increases window and slows it, so exclude 2.

3. Final Answer:

   Apply changes 1, 3, and 4 -> Option B

4. Quick Check:

   Reduce size, min_count, epochs = faster training [OK]

Hint: Lower vector_size, min_count, epochs to speed up [OK]

Common Mistakes:

• Increasing window size slows training
• Ignoring min_count effect on vocabulary size
• Reducing epochs too much hurts quality