mirror of
https://github.com/MLSysBook/TinyTorch.git
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45a9cef548
- Removed 01_setup module (archived to archive/setup_module) - Renumbered all modules: tensor is now 01, activations is 02, etc. - Added tito setup command for environment setup and package installation - Added numeric shortcuts: tito 01, tito 02, etc. for quick module access - Fixed view command to find dev files correctly - Updated module dependencies and references - Improved user experience: immediate ML learning instead of boring setup
29 lines
989 B
YAML
29 lines
989 B
YAML
description: Text processing systems that convert raw text into numerical sequences
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for language models
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estimated_time: 4-5 hours
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exports:
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- CharTokenizer
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- BPETokenizer
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- TokenizationProfiler
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- OptimizedTokenizer
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learning_objectives:
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- Implement character-level tokenization with special token handling
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- Build BPE (Byte Pair Encoding) tokenizer for subword units
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- 'Understand tokenization trade-offs: vocabulary size vs sequence length'
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- Optimize tokenization performance for production systems
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- Analyze how tokenization affects model memory and training efficiency
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ml_systems_focus: Text processing pipelines, tokenization throughput, memory-efficient
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vocabulary management
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name: Tokenization
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next_modules:
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- 12_embeddings
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number: 11
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prerequisites:
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- 02_tensor
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systems_concepts:
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- Memory efficiency of token representations
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- Vocabulary size vs model size tradeoffs
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- Tokenization throughput optimization
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- String processing performance
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- Cache-friendly text processing patterns
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