Tokenizing

Tokenization is a critical process in natural language processing (NLP) that transforms long strings of characters into smaller units called tokens. These tokens can be words, parts of words, or subwords, depending on the specific approach used.

Types of Tokenization

1. Word-Level Tokenization:

   • Splits text based on spaces or punctuation.
   • Simple and intuitive but struggles with unknown words during production 1.

2. Character-Level Tokenization:

   • Breaks text into individual characters.
   • Handles unknown words but is inefficient and often lacks meaningful representation for individual tokens 1.

3. Subword Tokenization:

   • Combines the advantages of word and character tokenization.
   • Efficiently handles unknown words by breaking them into meaningful subwords 1.
   • Byte Pair Encoding (BPE) is a popular method for subword tokenization, used in models like BERT and GPT-3. It starts with word-level tokens, breaks them into characters, and then merges frequently co-occurring characters to form subwords 2.

Key Concepts

• Tokens: The smaller units resulting from tokenization.
• Byte Pair Encoding (BPE): A technique to create subwords based on the frequency of character pairs, enhancing the model's efficiency and ability to handle unknown words 2.

Tokenization balances efficiency and representation, enabling NLP models to process text effectively across different languages and contexts.