BERT tokenization (WordPiece) in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does WordPiece handle unknown words?

When BERT's WordPiece tokenizer encounters a word not in its vocabulary, what does it do?

A It replaces the entire word with a special token [PAD].

B It breaks the word into smaller known subword units until all parts are recognized or uses [UNK] if no parts match.

C It ignores the word and removes it from the input sequence.

D It treats the whole word as a single token even if unknown.

💡 Hint

Think about how WordPiece tries to represent words using pieces it knows.

WordPiece splits unknown words into smaller subwords that exist in its vocabulary. If no subwords match, it uses the [UNK] token.

❓ Predict Output

intermediate

Output tokens from WordPiece tokenizer

Given the input sentence "unaffable", what is the output token list from BERT's WordPiece tokenizer?

NLP

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokens = tokenizer.tokenize('unaffable')
print(tokens)

A ["un", "aff", "able"]

B ["unaffable"]

C ["una", "##ffa", "##ble"]

D ["un", "##aff", "##able"]

💡 Hint

Look for subwords starting with ## indicating continuation pieces.

WordPiece splits "unaffable" into "un", "##aff", and "##able" tokens, where "##" marks subword pieces continuing a word.

❓ Model Choice

advanced

Choosing tokenizer for domain-specific text

You want to fine-tune BERT on medical text with many rare terms. Which tokenizer approach is best?

A Use a whitespace tokenizer that splits only on spaces.

B Use a character-level tokenizer that splits every character.

C Train a new WordPiece tokenizer on the medical corpus to capture rare terms better.

D Use the original BERT WordPiece tokenizer without changes.

💡 Hint

Think about how to handle many rare or new words effectively.

Training a new WordPiece tokenizer on domain-specific text helps capture rare terms as meaningful subwords, improving tokenization quality.

❓ Metrics

advanced

Measuring tokenization efficiency

You compare two tokenizers on the same text. Tokenizer A produces 100 tokens; Tokenizer B produces 130 tokens. Which statement is true about tokenization efficiency?

A Tokenizer A is more efficient because it uses fewer tokens to represent the text.

B Tokenizer B is more efficient because it produces more tokens for detail.

C Both are equally efficient because token count does not matter.

D Tokenizer B is more efficient because more tokens mean better accuracy.

💡 Hint

Fewer tokens usually mean less computation and simpler input.

Fewer tokens mean less input length, which usually leads to faster processing and less memory use, so Tokenizer A is more efficient.

🔧 Debug

expert

Why does this WordPiece tokenization code raise an error?

Consider this code snippet:

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokens = tokenizer.tokenize(12345)
print(tokens)

What error does this code raise?

A TypeError because the input to tokenize must be a string, not an integer.

B ValueError because the number 12345 is out of vocabulary range.

C KeyError because 12345 is not a token in the vocabulary.

D No error; it tokenizes the number as a string.

💡 Hint

Check the input type expected by the tokenizer.

The tokenizer expects a string input. Passing an integer causes a TypeError.