How Large is GPT-3 Dataset?
GPT-3 is a highly complex language model that is trained on a large dataset of text data. GPT or Generative Pre-trained Transformer is designed to be able to understand and generate human-like text.
Its enormously large dataset of text sources includes books, articles and websites.
It is difficult to provide an estimate of the percentage breakdown of sources used to train GPT-3.
However, some estimates claim that around 62% of all sources come from common internet crawling. Web text constitute around 19%, while books contribute roughly 15% and Wikipedia 4%.
But how large is GPT-3 dataset?
To give you an idea of the scale, one example of a GPT model was trained on a dataset of over 40 terabytes of text data.
This model is known as GPT-3, which is one of the largest publicly available pre-trained models.
Its dataset is several orders of magnitude larger than the entire text of Wikipedia.
For comparison, the English version of Wikipedia contains around 5 million articles and is around 50 GB in size.
So the dataset used to train GPT-3 is almost 1000 times larger than the text of Wikipedia.
Here's another example that gives you an idea of the complexity of GPT-3.
It is estimated that the average person takes in around 34 gigabytes of information throughout their lifetime.
In comparison, GPT-3 is trained on a dataset of 570 gigabytes of text data.
In other words, GPT-3 has been exposed to approximately 16 times more information than the average person throughout their entire lifetime.
Furthermore, GPT-3 is able to process and understand this information in a way that is similar to a human, making it capable of generating highly complex and nuanced text.
It's worth noting that since the release of GPT-3, other models with even larger dataset like GPT-4 and GPT-5 are in the making.
These models will be trained on even larger datasets and will be able to achieve far more superior results.