Modeling Encoder-Decoder according to instructions from a paper

I am new to this field and I was reading a paper Predicting citation counts based on deep neural network learning techniques. There the authors describe the code that they implemented if someone wants to reproduce the results. I tried to do this but I am not sure if I succeeded.

Here is their description:

-RNN module - SimpleRNN
-Output dimension of the encoder - 512
-The output layer - Dense layer
-Activation function - ReLU
-Overfitting prevention technique - Dropout with 0.2 rate
-Epochs - 100
Optimization algorithm - RMSProp
Learning rate - 10^{-5}
Batch size - 256

And here is my implementation. I am not sure if the model I created is sequence to sequence.

epocsh = 100
batch_size = 256
optimizer = keras.optimizers.RMSprop(lr=0.00001)
model =  keras.models.Sequential([
    keras.layers.SimpleRNN(512, input_shape=[X_train.shape[0], X_train.shape[1]],
                           activation='relu', return_sequences=True, dropout=0.2),
    keras.layers.Dense(9)
])

model.compile(loss='mse', optimizer=optimizer, metrics=[keras.metrics.RootMeanSquaredError()])

The summary of this model is:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
simple_rnn (SimpleRNN)       (None, 154521, 512)       266240    
_________________________________________________________________
dense (Dense)                (None, 154521, 9)         4617      
=================================================================
Total params: 270,857
Trainable params: 270,857
Non-trainable params: 0
_________________________________________________________________

Any help would be appreciated. Thank you!

Update: Here is the dataset.

year  venue  c1  c2  c3  c4  c5  c6  c7  c8  c9  c10  c11  c12  c13  c14
1989    234   0   1   2   3   4   5   5   5   5    8    8   10   11   12
1989    251   0   0   0   0   0   0   0   0   0    0    0    0    0    0
1990    346   0   0   0   0   0   0   0   0   0    0    0    0    0    0

I need to give as an input all the columns until c5, and try to predict the other c's (which are citation count for the upcoming years).