Electroencephalogram (EEG) based sleep stage classification is very important in sleep quality analysis and the treatment of sleep disorders. Deep learning based automated sleep staging has achieved promising performance. However, it has not been widely adopted in clinical practice, due to the domain shift problem and insufficient labeled training data, especially for patients. To cope with these problems, this paper proposes a Transformer-based semi-supervised domain adaptation (SSDA) approach for EEG-based sleep stage classification. It uses two class tokens to extract knowledge from the source and target domains separately. Then, a training-test strategy with adaptive entropy-weighted ensemble, attention-based adaptation and consistency regularization is used to improve the target domain performance. Experiments on two public datasets demonstrated the effectiveness of the proposed approach.