Summary of Batching Bpe Tokenization Merges, by Alexander P. Morgan
Batching BPE Tokenization Merges
by Alexander P. Morgan
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Byte Pair Encoding algorithm can be efficiently processed by batching hundreds of pairs of tokens, enabling high-quality tokenizer training on basic laptops. This paper introduces BatchBPE, an open-source Python implementation that makes exploring new tokenization strategies more accessible in resource-constrained contexts. By combining batched BPE with reduced memory footprint vocabulary training, researchers can experiment with preprocessing techniques like stop word lists and ignoring rare text chunks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BatchBPE is a tool that helps with building vocabularies for tokenizers by processing lots of pairs of words together. This makes it possible to train a good tokenizer even on simple computers. The idea is to make it easy for people to try out new ways of preparing text data, which can be useful in situations where there isn’t much computing power or memory available. |
Keywords
» Artificial intelligence » Tokenization » Tokenizer
