In this paper, the small-signal modeling of the Indium Phosphide High Electron Mobility Transistor (InP HEMT) based on the Transformer neural network model is investigated. The AC S-parameters of the HEMT device are trained and validated using the Transformer model. In the proposed model, the eight layers transformer encoders are connected in series and the encoder layer of each Transformer consists of the multi-head attention layer and the feed-forward neural network layer. The experimental results show that the measured and modeled S-parameters of the HEMT device match well in the frequency range of 0.5-40 GHz, with the errors versus frequency less than 1%, and the average error within 0.25%. Compared with other models, good accuracy can be achieved to verify the effectiveness of the proposed model.