description: Text processing systems that convert raw text into numerical sequences
  for language models
estimated_time: 4-5 hours
exports:
- CharTokenizer
- BPETokenizer
- TokenizationProfiler
- OptimizedTokenizer
learning_objectives:
- Implement character-level tokenization with special token handling
- Build BPE (Byte Pair Encoding) tokenizer for subword units
- 'Understand tokenization trade-offs: vocabulary size vs sequence length'
- Optimize tokenization performance for production systems
- Analyze how tokenization affects model memory and training efficiency
ml_systems_focus: Text processing pipelines, tokenization throughput, memory-efficient
  vocabulary management
name: Tokenization
next_modules:
- 12_embeddings
number: 11
prerequisites:
- 02_tensor
systems_concepts:
- Memory efficiency of token representations
- Vocabulary size vs model size tradeoffs
- Tokenization throughput optimization
- String processing performance
- Cache-friendly text processing patterns