Hi! The problem here is that your config object has default layer numbers and sizes that are totally different from the ones in albert-base-v2. If you’d like to train a sequence classification model on top of Albert, you can just do:
model = TFAlbertForSequenceClassification.from_pretrained('albert-base-v2', num_labels=2)

Alternatively, if you want to use a config object, you should initialize it from albert-base-v2 like this:
model = TFAlbertForSequenceClassification.from_pretrained('albert-base-v2', config=AlbertConfig.from_pretrained('albert-base-v2', problem_type="single_label_classification"))