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-layer 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%. Compared with other models， good accuracy can be achieved to verify the effectiveness of the proposed model.