If I load  the model from pretrained without much in the way of configs, I get about 11 million parameters in the albert main layer. If I load it but change the problem type and set it to ignore mismatched sizes, the main layer has 222 million parameters. This seems strange to me, I thought changing the problem type would only effect the classifier?
model = TFAlbertForSequenceClassification.from_pretrained('albert-base-v2', config=AlbertConfig(problem_type="single_label_classification"), ignore_mismatched_sizes=True)