Vocabulary size control in NLP - Model Pipeline Trace

Model Pipeline - Vocabulary size control

This pipeline shows how controlling vocabulary size helps manage text data for machine learning. It reduces the number of unique words to focus on the most important ones, making models faster and simpler.

Data Flow - 5 Stages

1Raw Text Input

1000 sentences x variable length→Collect raw text data from documents→1000 sentences x variable length

"I love apples", "Machine learning is fun"

↓

2Tokenization

1000 sentences x variable length→Split sentences into words (tokens)→1000 sentences x variable length tokens

["I", "love", "apples"], ["Machine", "learning", "is", "fun"]

↓

3Build Vocabulary

1000 sentences x variable length tokens→Count unique words and their frequencies→Vocabulary dictionary with word counts

{"I": 50, "love": 30, "apples": 20, "Machine": 40, "learning": 40, "is": 60, "fun": 25}

↓

4Vocabulary Size Control

Vocabulary dictionary with 5000 unique words→Keep top 1000 most frequent words, replace others with <UNK>→Vocabulary dictionary with 1000 words + <UNK>

Top words: {"I", "is", "Machine", ...}, others replaced by <UNK>

↓

5Text to Indexed Tokens

1000 sentences x variable length tokens→Replace words with their index in controlled vocabulary→1000 sentences x variable length indices

[1, 5, 20], [100, 200, 3, 15]

Training Trace - Epoch by Epoch

Loss
1.0 |          *
0.8 |         **
0.6 |        ***
0.4 |       ****
0.2 |      *****
    +----------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Model starts learning with controlled vocabulary
2	0.65	0.72	Loss decreases and accuracy improves as model learns
3	0.50	0.80	Model converges well with reduced vocabulary size
4	0.45	0.83	Further improvement, stable training
5	0.42	0.85	Training converged with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input Sentence

Layer 2: Vocabulary Mapping

Layer 3: Model Input Layer

Layer 4: Prediction Output

Model Quiz - 3 Questions

Why do we limit vocabulary size in text processing?

A To increase the number of unique words

B To reduce model complexity and focus on important words

C To make sentences longer

D To remove all rare words permanently

Key Insight

Controlling vocabulary size helps the model focus on the most frequent and important words. This reduces complexity and speeds up learning, leading to better and faster training convergence.

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