Vocabulary size control in NLP

Introduction

Controlling vocabulary size helps models focus on important words and run faster by ignoring rare or unimportant words.

When building a text classifier and you want to reduce noise from rare words.

When training a language model and you need to limit memory use.

When preparing text data for chatbots to keep the model simple.

When working with limited computing power and want faster training.

When you want to improve model generalization by ignoring very rare words.

Syntax

NLP

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer(max_features=VOCAB_SIZE)
X = vectorizer.fit_transform(texts)

max_features sets the maximum number of words to keep based on frequency.

Only the top VOCAB_SIZE most common words are kept in the vocabulary.

Examples

Keeps only the 1000 most frequent words from the text.

NLP

vectorizer = CountVectorizer(max_features=1000)

Keeps up to 500 words that appear in at least 5 documents.

NLP

vectorizer = CountVectorizer(max_features=500, min_df=5)

Keeps top 2000 words excluding common English stop words.

NLP

vectorizer = CountVectorizer(max_features=2000, stop_words='english')

Sample Model

This example limits the vocabulary to the top 5 words by frequency from the sample texts. It then shows the vocabulary and the transformed feature matrix.

NLP

from sklearn.feature_extraction.text import CountVectorizer

texts = [
    'I love machine learning',
    'Machine learning is fun',
    'I love coding in Python',
    'Python coding is great for machine learning'
]

VOCAB_SIZE = 5
vectorizer = CountVectorizer(max_features=VOCAB_SIZE)
X = vectorizer.fit_transform(texts)

print('Vocabulary:', vectorizer.get_feature_names_out())
print('Transformed shape:', X.shape)
print('Feature matrix (dense):\n', X.toarray())

Important Notes

Choosing a very small vocabulary size may lose important words and reduce model accuracy.

Using max_features keeps the most frequent words, which usually carry more meaning.

You can combine vocabulary size control with stop word removal for cleaner data.

Summary

Vocabulary size control limits the number of words the model uses.

It helps speed up training and reduce noise from rare words.

Use max_features in vectorizers to set vocabulary size easily.

Practice

1. What is the main purpose of controlling vocabulary size in NLP models?

easy

A. To add more rare words to the dataset

B. To increase the number of training epochs

C. To limit the number of words the model uses

D. To make the model ignore stop words

Solution

1. Step 1: Understand vocabulary size control

   Vocabulary size control means setting a limit on how many unique words the model can use.

2. Step 2: Identify the main goal

   The goal is to reduce complexity and noise by ignoring very rare words, so the model focuses on common words.

3. Final Answer:

   To limit the number of words the model uses -> Option C

4. Quick Check:

   Vocabulary size control = limit words [OK]

Hint: Vocabulary size control means limiting words used [OK]

Common Mistakes:

• Thinking it increases training epochs
• Believing it adds rare words
• Confusing it with stop word removal

2. Which parameter in scikit-learn's CountVectorizer controls the vocabulary size?

easy

A. max_features

B. min_df

C. stop_words

D. ngram_range

Solution

1. Step 1: Recall CountVectorizer parameters

   CountVectorizer has parameters like max_features, min_df, stop_words, and ngram_range.

2. Step 2: Identify parameter for vocabulary size

   max_features sets the maximum number of words (features) to keep, controlling vocabulary size.

3. Final Answer:

   max_features -> Option A

4. Quick Check:

   max_features controls vocabulary size [OK]

Hint: max_features sets max vocabulary size in vectorizers [OK]

Common Mistakes:

• Choosing min_df which filters by document frequency
• Confusing stop_words with vocabulary size
• Thinking ngram_range controls vocabulary size

3. What will be the output vocabulary size after running this code?

from sklearn.feature_extraction.text import CountVectorizer
texts = ['apple banana apple', 'banana orange', 'apple orange orange']
vectorizer = CountVectorizer(max_features=2)
vectorizer.fit(texts)
vocab = vectorizer.get_feature_names_out()
print(len(vocab))

medium

A. 3

B. 2

C. 4

D. 1

Solution

1. Step 1: Understand max_features effect

   max_features=2 means the vectorizer keeps only the top 2 most frequent words.

2. Step 2: Count unique words and frequencies

   Words: apple(3), banana(2), orange(3). Top 2 are apple and orange.

3. Final Answer:

   2 -> Option B

4. Quick Check:

   max_features=2 means vocabulary size = 2 [OK]

Hint: max_features limits vocabulary count to given number [OK]

Common Mistakes:

• Counting all unique words ignoring max_features
• Assuming max_features is minimum count
• Confusing frequency with vocabulary size

4. Identify the error in this code snippet that tries to limit vocabulary size:

from sklearn.feature_extraction.text import CountVectorizer
texts = ['cat dog', 'dog mouse', 'cat mouse']
vectorizer = CountVectorizer(max_features='3')
vectorizer.fit(texts)
vocab = vectorizer.get_feature_names_out()
print(vocab)

medium

A. max_features should be an integer, not a string

B. fit() should be replaced with fit_transform()

C. get_feature_names_out() is deprecated

D. texts should be a numpy array

Solution

1. Step 1: Check max_features type

   max_features expects an integer, but '3' is a string, causing a type error.

2. Step 2: Confirm other parts are correct

   fit() works fine, get_feature_names_out() is current method, texts can be list.

3. Final Answer:

   max_features should be an integer, not a string -> Option A

4. Quick Check:

   max_features type must be int [OK]

Hint: max_features must be int, not string [OK]

Common Mistakes:

• Using string instead of integer for max_features
• Thinking fit_transform is required here
• Believing get_feature_names_out is deprecated

5. You want to build a text classifier but your dataset has 100,000 unique words. To speed up training and reduce noise, which approach best controls vocabulary size?

hard

A. Increase max_features to 200,000 to include more words

B. Use all 100,000 words to keep maximum information

C. Remove stop words only without limiting vocabulary size

D. Set max_features to a smaller number like 5000 in your vectorizer

Solution

1. Step 1: Understand problem with large vocabulary

   100,000 words is large and slows training; many words may be rare and noisy.

2. Step 2: Choose best vocabulary control method

   Setting max_features to a smaller number like 5000 keeps common words and speeds training.

3. Final Answer:

   Set max_features to a smaller number like 5000 in your vectorizer -> Option D

4. Quick Check:

   Limit vocabulary size to speed training [OK]

Hint: Limit vocabulary size to speed training and reduce noise [OK]

Common Mistakes:

• Using all words causing slow training
• Only removing stop words without size control
• Increasing max_features unnecessarily