counting number of parameters keras
In fact, you use 1D convolution. 
Given that the dimension of the output of embedding layer is 100, that the kernel size is 5, and that the number of filters is 128,
You have 100x5x128 = 64000 weights. Add to this 128 biases and you get 64128 parameters.

Note that parameter sharing is used, so that there is only one set of weights and biases per filter, in depth.