# Module 11: Tokenization - Text Processing for Language Models

## Overview
This module implements the fundamental text processing systems that convert raw text into numerical sequences that neural networks can understand. You'll build character-level and subword tokenizers from scratch, understanding the critical trade-offs between vocabulary size and sequence length that affect model performance.

## What You'll Learn

### Core Implementations
- **Character Tokenizer**: Simple character-level tokenization with special tokens
- **BPE Tokenizer**: Byte Pair Encoding for efficient subword units
- **Vocabulary Management**: Bidirectional mappings between text and indices
- **Padding & Truncation**: Batch processing utilities for uniform sequences

### ML Systems Concepts
- **Memory Efficiency**: How vocabulary size affects model parameters
- **Performance Optimization**: Tokenization throughput and caching strategies
- **Scaling Trade-offs**: Vocabulary size vs sequence length vs compute
- **Production Patterns**: Efficient text processing for large-scale systems

### Performance Engineering
- **Tokenization Profiling**: Measuring speed and memory usage
- **Cache Optimization**: Reducing repeated tokenization overhead
- **Batch Processing**: Efficient handling of multiple texts
- **Scaling Analysis**: Understanding performance with large texts

## Key Learning Outcomes

By completing this module, you'll understand:

1. **Text-to-Numbers Pipeline**: How raw text becomes neural network input
2. **Tokenization Strategies**: Character vs subword vs word-level approaches
3. **Systems Trade-offs**: Vocabulary size impacts on memory and compute
4. **Performance Engineering**: Optimizing text processing for production
5. **Language Model Foundation**: How tokenization affects model capabilities

## Files in This Module

- `tokenization_dev.py` - Main implementation file with all tokenizers
- `tokenization_dev.ipynb` - Jupyter notebook (auto-generated)
- `module.yaml` - Module configuration and metadata
- `README.md` - This documentation file

## Usage Example

```python
from tinytorch.core.tokenization import CharTokenizer, BPETokenizer

# Character-level tokenization
char_tokenizer = CharTokenizer()
tokens = char_tokenizer.encode("Hello world!")
text = char_tokenizer.decode(tokens)

# BPE tokenization
bpe_tokenizer = BPETokenizer(vocab_size=1000)
bpe_tokenizer.train(["Hello world", "World hello", "Hello hello world"])
tokens = bpe_tokenizer.encode("Hello world!")
```

## Integration with TinyTorch

This module exports to `tinytorch.core.tokenization` and provides the text processing foundation for:
- **Embedding layers** (Module 12) - Converting tokens to vectors
- **Language models** (Module 14+) - Processing text sequences
- **Training pipelines** - Efficient batch text processing

## Systems Engineering Focus

This module emphasizes the systems engineering aspects of tokenization:

### Performance Characteristics
- **Character tokenization**: Small vocab (~256), long sequences
- **BPE tokenization**: Medium vocab (~50k), shorter sequences  
- **Memory scaling**: O(vocab_size × embedding_dim) for embedding tables
- **Attention scaling**: O(sequence_length²) for transformer models

### Production Considerations
- Tokenization can become a bottleneck in training pipelines
- Efficient string processing is critical for high-throughput systems
- Caching strategies provide significant speedups for repeated texts
- Vocabulary size affects model download size and memory usage

## Prerequisites
- Module 02: Tensor (for basic data structures)
- Understanding of string processing and algorithms

## Estimated Time
4-5 hours including implementation, testing, and analysis

## Next Steps
After completing this module, you'll be ready for:
- **Module 12: Embeddings** - Converting tokens to dense vector representations
- **Module 13: Attention** - Processing sequences with attention mechanisms
- **Module 14: Transformers** - Complete language model architectures