--- a
+++ b/src/hybrid/hybrid.py
@@ -0,0 +1,83 @@
+import torch 
+import torch.nn as nn
+from src.rnn.rnn_utils import create_emb_layer
+
+
+class hybrid(nn.Module):
+  def __init__(self, vocabulary, sequence_length, weights_matrix, hidden_size, num_layers=2, num_classes=10):
+    super().__init__()
+
+    self.num_layers = num_layers
+    self.hidden_size = hidden_size
+
+    self.conv1 = nn.Sequential(nn.Conv1d(len(vocabulary)+1,
+                                            128,
+                                            kernel_size=7,
+                                            padding=0),
+                                  nn.ReLU(),
+                                  nn.MaxPool1d(3)
+                                  )
+
+    self.conv2 = nn.Sequential(nn.Conv1d(128, 128, kernel_size=7, padding=0),
+                                nn.ReLU(),
+                                nn.MaxPool1d(3)
+                                )
+
+    self.conv3 = nn.Sequential(nn.Conv1d(128, 128, kernel_size=3, padding=0),
+                                nn.ReLU()
+                                )
+
+    self.conv4 = nn.Sequential(nn.Conv1d(128, 128, kernel_size=3, padding=0),
+                                nn.ReLU()
+                                )
+    input_shape = (1, len(vocabulary)+1, sequence_length)
+    self.output_dimension = self._get_conv_output(input_shape)
+
+    # define linear layers
+
+    self.fc1 = nn.Sequential(
+        nn.Linear(self.output_dimension, 256),
+        nn.ReLU(),
+    )
+
+      
+    self.embeddings, num_embeddings, embedding_size = create_emb_layer(weights_matrix, True)
+    self.gru1 = nn.GRU(embedding_size, hidden_size, num_layers, bidirectional = True, batch_first=True)
+        
+    self.fc2 = nn.Sequential(
+        nn.Linear(2*hidden_size, 256),
+        nn.ReLU(),
+    ) 
+
+
+    self.fc3 = nn.Linear(512,10)   
+    self.act = nn.Sigmoid()
+
+
+  def _get_conv_output(self, shape):
+        x = torch.rand(shape)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = x.view(x.size(0), -1)
+        output_dimension = x.size(1)
+        return output_dimension
+  
+  def forward(self,rnninput, cnninput):
+    cnn_out = self.conv1(cnninput)
+    cnn_out = self.conv2(cnn_out)
+    cnn_out = self.conv3(cnn_out)
+    cnn_out = self.conv4(cnn_out)
+    cnn_out = cnn_out.view(cnn_out.size(0),-1)
+    cnn_out = self.fc1(cnn_out)
+
+    rnn_out = self.embeddings(rnninput)
+    rnn_out,_ = self.gru1(rnn_out)
+    rnn_out = self.fc2(rnn_out[:,-1,:])
+
+
+    x = torch.cat((cnn_out,rnn_out),dim=1)
+    out = self.fc3(x)
+    out = self.act(out)
+    return out