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AI

Mar 2025×11 min read

Learn about BERT's unique approach to tokenization. Discover the WordPiece algorithm and why BERT needs special tokens like [CLS] and [SEP].

BERT Tokenization: WordPiece & Special Tokens 🦷

Driptanil DattaSoftware Developer

BERT Tokenization: WordPiece & Special Tokens 🦷

While GPT uses Byte Pair Encoding (BPE), BERT uses a similar but distinct algorithm called WordPiece. BERT is also an "encoder-only" model designed for understanding context, which requires a few special additions to the token sequence.

1. WordPiece vs. BPE

WordPiece works similarly to BPE by merging frequent pairs, but it uses a likelihood-based approach rather than simple frequency. One signature of BERT's WordPiece is the ## prefix, which denotes a subword that is part of a larger word.

from transformers import BertTokenizer
 
# Load the base uncased BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 
# Check vocabulary size
print(f"BERT Vocab Size: {tokenizer.vocab_size}")

BERT Vocab Size: 30522

Notice that BERT's vocabulary (30k) is significantly smaller than GPT-4's (100k).

2. Special Tokens: [CLS] and [SEP]

BERT adds specific markers to the beginning and end of every sequence:

• [CLS] (ID 101): The "Classification" token. It's meant to represent the summary of the entire sentence.
• [SEP] (ID 102): The "Separation" token. It marks the end of a sentence or the boundary between two sentences.

text = "science is great"
encoded = tokenizer.encode(text)
 
for i in encoded:
  print(f"ID {i} -> '{tokenizer.decode([i])}'")

ID 101 -> '[CLS]'
ID 2671 -> 'science'
ID 2003 -> 'is'
ID 2307 -> 'great'
ID 102 -> '[SEP]'

3. Subword Segmentation

Let's see how BERT handles words that aren't in its primary vocabulary.

sentence = "AI is both exciting and terrifying."
tokens = tokenizer.tokenize(sentence)
 
print(f"Tokens: {tokens}")

Tokens: ['ai', 'is', 'both', 'exciting', 'and', 'terrifying', '.']

If we used a more complex word like "unbelievably":

tokenizer.tokenize("unbelievably")
# ['un', '##believ', '##ably']

The ## tells the model that these chunks are connected to the preceding token.

4. Why the Difference?

💡 Summary

BERT's tokenization is tailored for natural language understanding. The inclusion of [CLS] and [SEP] allows the model to process relationships between sentences and build a global representation of the input.

In the next lesson, we'll dive deeper into BERT's character-level handling and the [MASK] token!