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How text gets split into tokens is determined by something called a tokenizer, and the way it splits can vary depending on the system or model.
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🔍 Tokens ≠ Words
Here’s the key idea:
Tokens can be whole words, parts of words, or even punctuation marks.
This is especially true for systems like OpenAI’s GPT or Google’s T5, which use what's called subword tokenization.
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✅ Example: “unbelievable!”
Depending on the tokenizer, this might be split as:
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And yes — the exclamation mark is its own token too!
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✨ Why So Granular?
Tokenizing this way helps models:
Understand unfamiliar or rare words by breaking them into known parts.
Handle different languages or spellings more effectively.
Compress text into consistent units that are easier to train on.
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💬 TL;DR
A token is a unit of text used by language models.
It might be a word, part of a word, or even punctuation.
This depends on how the text is broken down by the tokenizer, which is an essential part of how LLMs work.
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