How do you calculate tokensation

Converting from words or characters to tokens is crucial when working with language models like GPT because they process input and output in tokens rather than raw words or characters. The conversion can vary based on the language model (LLM) and the specific tokenizer being used. Here's how the process generally works:

1. Understanding Tokens in GPT Models

Tokens can represent words, word pieces, or characters, depending on the complexity of the word.
For English text, one token is typically about 4 characters of text, including spaces, or about ¾ of a word on average. However, shorter words (like "a" or "I") can be single tokens, and longer or compound words may be split into multiple tokens.

2. Tokenization Across GPT Models

GPT-3.5, GPT-4, and other OpenAI models use a Byte Pair Encoding (BPE) tokenizer. This tokenizer splits text into sub-word units, which means that common words are single tokens, but longer or less common words are broken into multiple tokens.
Claude (Anthropic), BERT, and other models also use tokenization methods that may break words differently, but the general principles of tokenization apply across most modern LLMs.

3. Conversion from Characters or Words to Tokens

Approximate conversions for GPT models:

Characters to Tokens:
1 token ≈ 4 characters of English text.
Example: "ChatGPT is awesome" (17 characters, including spaces) ≈ 5 tokens.
Words to Tokens:
1 token ≈ ¾ of a word in English.
Example: "This is a test" (4 words) ≈ 5 tokens.

Note: The exact number of tokens depends on the language, punctuation, and specific text structure.

4. Examples of Tokenization: