This work quantifies the extent to which accuracy degrades on review classification when state-of-the-art Transformer models are subjected to distribution shifts, and offers a solution to significantly decrease this degradation. We find differences in the extent of degradation depending on the independent variable across which the shift is created. Specifically, in our experiments time and sentiment shifts show upto 10% drops in accuracy; whereas shifts between industry and product sectors show 20-40% drops in accuracy. We provide ablation experiments with different Transformer architectures, such as BERT, T5 and Jurassic-I, and study their relationship with this degradation. The suggested solution reuses the base of the model trained on one distribution, in addition to fine-tuning the final dense layer in the model to support the new distribution that is seen once the model is deployed. This uses just 100-300 samples compared to the previous 10,000 samples from the unseen distribution, while decreasing the accuracy drops in half.