Tokenizer

Demos

• https://tiktokenizer.vercel.app/
• https://platform.openai.com/tokenizer

Are tokenizers hard coded or learned? Both exist:

1. Rule-Based (Hard-Coded) Tokenizers
   • Examples: Whitespace tokenizers, simple punctuation-based splitters
2. Learned (Trained) Tokenizers:
   • Byte Pair Encoding (used for GPT-4o) → tiktoken
   • SentencePiece by Google, a library

By learned, it's not necessarily a neural network

It’s just doing it on large corpus of text, running some sort of mapreduce job, doing a frequency count $O(n^2)$ of pairs, and then merging those pairs to introduce a new token.

• Source: https://www.youtube.com/watch?v=7xTGNNLPyMI

https://watml.github.io/slides/CS480680_lecture11.pdf

Special tokens

• [CLS] - Prepended to the beginning of the sequence in BERT-style models.
• [SEP] - Marks the end of a sentence/segment, or separates two segments.
• [MASK] - For masked language modeling
• [PAD - to pad so that sequences in a batch are the same length

Related

• Embedding