Super Data Science: ML & AI Podcast with Jon Krohn 626: Subword Tokenization with Byte-Pair Encoding
9 snips
Nov 11, 2022 The podcast discusses word, character, and subword tokenization in NLP, highlighting the benefits of subword tokenization. Byte pair encoding is explored as a key method in leading NLP models.
AI Snips
Chapters
Transcript
Episode notes
Word-Level Tokenization and its Limitations
- Word-level tokenization splits text into words, like "the cat sat" into "the", "cat", "sat".
- This approach struggles with unknown words in production, ignoring potentially important terms.
Character-Level Tokenization: Advantages and Disadvantages
- Character-level tokenization splits text into individual characters, handling unknown words.
- However, it requires many tokens and loses word-level meaning, impacting performance.
Subword Tokenization: A Balanced Approach
- Subword tokenization balances the strengths of word- and character-level methods.
- It efficiently represents words while handling out-of-vocabulary terms effectively.