A core solution for improving NLP word-splitting efficiency
Background Pain Points: In natural language processing tasks, traditional word-splitting tools are limited in speed when processing GB-level text, which seriously affects the efficiency of preprocessing.
Core Programs: Performance optimization using TokenDagger:
- Using the PCRE2 regularization engine: by
sudo apt install libpcre2-devInstallation of dependency libraries, 3-5 times faster compared to standard implementations - Simplified BPE algorithm: reduce special token processing overhead and get 4x speedup for code text
- Parallel Processing Capability: Built-in optimization for batch text, 1GB file throughput increase of 300%
Implementation steps::
- Replace the original TikToken code: simply change the import statement to
from tokendagger import encoding_for_model - Chunking is recommended when dealing with long text:
chunks = [text[i:i+1000000] for i in range(0, len(text), 1000000)] - For code files it is preferred to use the
encoder.encode(code, is_code=True)Parameter activation optimization mode
This answer comes from the articleTokenDagger: High Performance Text Segmentation ToolThe
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