There is no definitive answer to this question, as different models may have different data requirements depending on their architecture, complexity and task. Some sources say that language models require only about 10 or 100 million words to learn representations that reliably encode most semantic features. A larger quantity of data is needed in order to grasp enough commonsense knowledge to master typical downstream natural language tasks.
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