Create model_arch.py

model_arch.py

@@ -0,0 +1,18 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+class TextClassifierModel(nn.Module):
+    def __init__(self, vocab_size, embed_size, num_class):
+        super(TextClassifierModel, self).__init__()
+
+        self.embedding = nn.EmbeddingBag(vocab_size, embed_size)
+        self.bn1 = nn.BatchNorm1d(embed_size)
+        self.fc = nn.Linear(embed_size, num_class)
+
+
+    def forward(self, text, offsets):
+        embedded = self.embedding(text, offsets)
+        embedded_norm = self.bn1(embedded)
+        embedded_activated = F.relu(embedded_norm)
+
+        return self.fc(embedded_activated)