I am using this model.

class CNN_TC(nn.Module):
def init(self,googleNews_vectors,Filters):
super().init()
self.embedding = nn.Embedding.from_pretrained(googleNews_vectors,freeze=True)
self.embedding.weight.requires_grad = False
self.conv1 = nn.Conv1d(1,1,kernel_size=(Filters[0] * 300),stride=(300))
self.conv2 = nn.Conv1d(1,1,kernel_size=(Filters[1] * 300),stride=(300))
self.conv3 = nn.Conv1d(1,1,kernel_size=(Filters[2] * 300),stride=(300))
    self.max_conv1 = nn.MaxPool1d(kernel_size=(15))
    self.max_conv2 = nn.MaxPool1d(kernel_size=(11))
    self.max_conv3 = nn.MaxPool1d(kernel_size=(9))
    
    self.FFN1 = nn.Linear(9,100)
    self.FFN2 = nn.Linear(100,2)
    
def forward(self,inputs):
    X = self.embedding(inputs)
    X = X.view(X.shape[0],1,-1).contiguous()

    max_feature1 = self.max_conv1(f.relu(self.conv1(X)))
    max_feature2 = self.max_conv2(f.relu(self.conv2(X)))
    max_feature3 = self.max_conv3(f.relu(self.conv3(X)))
    features = torch.cat((max_feature1,max_feature2,max_feature3), -1).squeeze(0)
                         
    output = self.FFN2(f.relu(self.FFN1(features)))
    return output