--- a
+++ b/src/rnn/lstm.py
@@ -0,0 +1,30 @@
+import torch 
+import torch.nn as nn
+from src.rnn.rnn_utils import create_emb_layer
+
+class LSTMw2vmodel(nn.Module) :
+  def __init__(self, weights_matrix, hidden_size, num_layers, device, num_classes = 10) :
+
+    super().__init__()
+    self.num_layers = num_layers
+    self.hidden_size = hidden_size
+    self.device = device
+    self.embeddings, num_embeddings, embedding_size = create_emb_layer(weights_matrix, True)
+    self.lstm = nn.LSTM(embedding_size, hidden_size, num_layers, batch_first=True, bidirectional = True)
+    self.fc1 = nn.Sequential(
+        nn.Linear(2*hidden_size, 128),
+        nn.ReLU(),
+    )
+    self.fc2 = nn.Linear(128, num_classes)
+    self.act = nn.Sigmoid()
+      
+      
+  def forward(self, x):     
+    x = self.embeddings(x)
+    h0 = torch.zeros(2*self.num_layers, x.size(0), self.hidden_size).to(self.device)
+    c0 = torch.zeros(2*self.num_layers, x.size(0), self.hidden_size).to(self.device)
+    lstm_out, (ht, ct) = self.lstm(x, (h0,c0))
+
+    out = self.fc1(lstm_out[:,-1,:])
+    out = self.fc2(out)
+    return self.act(out)
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