Hi… Thanks for the response.
Do you suggest any modification in following for getting better accuracy.
max_features = 1000
embed_size = 64

class Model(nn.Module):
  def __init__(self):
  super(Model, self).__init__()
       
 ## Embedding Layer, Add parameter 
  self.embedding = nn.Embedding(max_features, embed_size) 
  et = torch.tensor(embedding_matrix, dtype=torch.float32)
  self.embedding.weight = nn.Parameter(et)
  self.embedding.weight.requires_grad = False
  self.embedding_dropout = nn.Dropout2d(0.1)
  self.lstm = nn.LSTM(300, 40)        
  self.linear = nn.Linear(40, 16)
  self.out = nn.Linear(16, 1)
  self.relu = nn.ReLU()

 def forward(self, x):
   h_embedding = self.embedding(x)        
   h_lstm, _ = self.lstm(h_embedding)
   max_pool, _ = torch.max(h_lstm, 1)        
   linear = self.relu(self.linear(max_pool))
   out = self.out(linear)
   return out

model = Model()