[nightski]

You act like training is this pre-set process you just "do". That's not the case, you train until you reach desired performance on the test set. If you don't have a test set how do you know when to stop training and avoid overfitting?

[baobabKoodaa]

You're confusing training epochs with dataset size.

I'm simplifying now, but you can think of epochs as "how many times we train over the entire dataset? 1 time? 10 times?"

Correspondingly, you can think of dataset size as "how many Wikipedia pages we include in the dataset? 1 million? 10 million?"

Now let's think about overfitting.

What happens when you increase epochs is the model is more likely to overfit your data.

What happens when you increase dataset size is the model is less likely to overfit your data.