Creating the sentiment predictor

Now, we will use the AutoKeras TextClassifier to find the best classification model. Just for this example, we will set max_trials (the maximum number of different Keras models to try) to 2; we do not need to set the epochs parameter; instead, we must define an EarlyStopping callback of 2 epochs so that the training process stops if the validation loss does not improve in two consecutive epochs:

clf = ak.TextClassifier(max_trials=2)
cbs = [tf.keras.callbacks.EarlyStopping(patience=2)]

Let's run the training process and search for the optimal classifier for the training dataset:

clf.fit(x_train, y_train, callbacks=cbs)

Here is the output:

Figure 7.3 – Notebook output of text classifier training

The previous output shows that the accuracy of the training dataset is increasing.

As we can see, we are getting a loss of 0.28 in the validation set. This isn't bad just for a few minutes of training...