Vocabulary size control in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is vocabulary size control in NLP?

Vocabulary size control is the process of limiting or managing the number of unique words or tokens used in a language model to improve efficiency and reduce complexity.

beginner

Why do we need to control vocabulary size in NLP models?

Controlling vocabulary size helps reduce memory use, speeds up training, and avoids rare words that add noise, making models more efficient and generalizable.

intermediate

Name two common methods to control vocabulary size.

1. Limiting vocabulary to the most frequent words. 2. Using subword units like Byte Pair Encoding (BPE) to break rare words into smaller parts.

intermediate

How does Byte Pair Encoding (BPE) help with vocabulary size control?

BPE merges frequent pairs of characters or subwords to create a compact vocabulary that can represent rare words as combinations of smaller units, reducing total vocabulary size.

advanced

What is the trade-off when choosing a smaller vocabulary size?

A smaller vocabulary reduces model size and speeds training but may increase the number of tokens per sentence, potentially making sequences longer and harder to process.

What happens if vocabulary size is too large in an NLP model?

A The model uses more memory and trains slower

B The model becomes faster and smaller

C The model ignores rare words

D The model always improves accuracy

A large vocabulary increases memory use and slows training because the model must handle many unique tokens.

Which method breaks words into smaller parts to reduce vocabulary size?

A One-hot encoding

B Stop word removal

C Lemmatization

D Byte Pair Encoding (BPE)

BPE splits rare words into subword units, reducing the total vocabulary needed.

Limiting vocabulary to the most frequent words helps because:

A It reduces noise and model size

B Rare words are always unimportant

C It increases the number of tokens per sentence

D It makes the model ignore common words

Focusing on frequent words reduces noise from rare words and keeps the vocabulary manageable.

What is a downside of using a very small vocabulary?

A More memory usage

B Longer token sequences

C Slower training

D Ignoring frequent words

Smaller vocabularies often mean words are split into many tokens, making sequences longer.

Vocabulary size control is important because:

A It always improves model accuracy

B It removes all rare words

C It balances model size and performance

D It makes models ignore punctuation

Controlling vocabulary size helps balance efficiency and model quality.

Explain vocabulary size control and why it matters in NLP models.

Think about how many words a model knows and how that affects speed and memory.

Describe two methods to control vocabulary size and their pros and cons.

Consider how each method handles rare words and vocabulary size.

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