Tokenization Fundamentals
Understanding how text is converted into tokens that language models can process
What is Tokenization?
Tokenization is the foundational process that converts human-readable text into a format that language models can understand and process. It breaks down text into smaller units called "tokens" - which can be characters, words, or subwords - and then maps these tokens to numerical IDs.
Every interaction with a language model starts with tokenization, making it one of the most critical components in the entire pipeline. Understanding tokenization is essential for optimizing model performance, managing costs, and debugging issues in production systems.
Interactive Tokenization Calculator
Core Tokenization Concepts
Types of Tokenization
Character-Level
Each character is a token. Simple but results in long sequences.
Example: "hello" → ["h", "e", "l", "l", "o"]
Word-Level
Whole words as tokens. Natural but struggles with OOV words.
Example: "hello world" → ["hello", "world"]
Subword-Level
Balances efficiency and vocabulary coverage using algorithms like BPE.
Example: "unhappiness" → ["un", "happy", "ness"]
Key Considerations
Vocabulary Size
Impacts model size, memory usage, and computational efficiency
Sequence Length
Determines maximum context the model can process
Special Tokens
[CLS], [SEP], [PAD], [UNK] provide structural information
Multilingual Support
Shared vocabularies enable cross-lingual understanding
Real-World Tokenization Examples
GPT Models
- • BPE tokenization with ~50k vocabulary
- • 1 token ≈ 4 characters on average
- • Context length: 2k-32k+ tokens
- • Pricing based on token count
BERT Models
- • WordPiece tokenization
- • 30k vocabulary size
- • 512 token sequence limit
- • Handles 104 languages (multilingual)
T5 & mT5
- • SentencePiece tokenization
- • 32k vocabulary (T5), 250k (mT5)
- • Text-to-text unified format
- • Strong multilingual performance
Implementation Examples
Tokenization Best Practices
✅ Do's
- •Use appropriate tokenizer for your model architecture
- •Monitor token usage to optimize costs in production
- •Consider domain-specific vocabulary for specialized tasks
- •Implement proper handling of special tokens
- •Validate tokenization consistency across environments
- •Use efficient tokenization libraries for production
❌ Don'ts
- •Mix different tokenizers for the same model
- •Ignore out-of-vocabulary handling in production
- •Assume tokenization is consistent across languages
- •Overlook the impact of vocabulary size on model performance
- •Forget to handle edge cases like empty strings
- •Use inefficient tokenization in high-throughput systems
Production Considerations
Performance Optimization
- • Batch Processing: Tokenize multiple texts together for efficiency
- • Caching: Cache tokenized results for frequently used texts
- • Parallel Processing: Use multi-threading for large datasets
- • Memory Management: Stream tokenization for very large texts
Cost Management
- • Token Counting: Accurate token estimation for API pricing
- • Sequence Optimization: Minimize padding and truncation
- • Vocabulary Pruning: Remove unused tokens to reduce model size
- • Compression: Use efficient encoding for storage and transmission