Why apply a 50:50 train test split? I am going through the "Text classification with TensorFlow Hub" tutorial.
In this tutorial, a total of 50,000 IMDb reviews are split into 25,000 reviews for training and 25,000 reviews for testing.

I am surprised by this way of splitting the data, since I learned in Andrew Ng's course that for fairly small datasets (&lt;10,000 examples) the "old-fashioned" rule of thumb was to consider 60% or 70% of the data as training examples and the remainder as dev/test examples.

Is there a reason behind this 50:50 split?


Is it common practice when working with text?
Does it have anything to do with using a "pre-trained" TensorFlow Hub layer?
Is it common practice when working with text?


No, you could split dataset as you wish, in general in real-world problem you should use cross validation.


  Does it have anything to do with using a "pre-trained" TensorFlow Hub layer?


No, it doesn't.