The invisible foundation that determines your model’s efficiency, multilingual capabilities, and training costs.
11 min readJust now
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Before a language model sees a single word, it needs to break text into pieces it can understand. This process, called tokenization, might seem like a technical detail, but it shapes everything about how a model learns and performs. Get it wrong, and you’ll waste compute training a model that struggles with basic tasks. Get it right, and you’ve laid the groundwork for a model that efficiently learns from data.
Let’s dig into how tokenization actually works and why these decisions matter.
Why We Need Subword Tokenization
The naive approach would be to treat each word as a token. But this cr…
The invisible foundation that determines your model’s efficiency, multilingual capabilities, and training costs.
11 min readJust now
–
Press enter or click to view image in full size
Before a language model sees a single word, it needs to break text into pieces it can understand. This process, called tokenization, might seem like a technical detail, but it shapes everything about how a model learns and performs. Get it wrong, and you’ll waste compute training a model that struggles with basic tasks. Get it right, and you’ve laid the groundwork for a model that efficiently learns from data.
Let’s dig into how tokenization actually works and why these decisions matter.
Why We Need Subword Tokenization
The naive approach would be to treat each word as a token. But this creates problems fast. English alone has hundreds of thousands of words, and that’s before you consider different forms (walk, walked, walking), typos, and domain-specific terms. A word-level tokenizer would need a massive vocabulary and would still encounter unknown words constantly.
The other extreme is character-level tokenization. This solves the unknown-word problem, since any text can be represented as a sequence of characters, but now simple words become very…