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decoder_model.py

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+
+# Your existing Decoder class code goes here
+# For example:
+import torch
+import torch.nn as nn
+
+class Decoder(nn.Module):
+    def __init__(self, embed_dim=768, num_heads=8, hidden_dim=1024, num_layers=2):
+        super(Decoder, self).__init__()
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=embed_dim,
+            nhead=num_heads,
+            dim_feedforward=hidden_dim
+        )
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.output_layer = nn.Linear(embed_dim, embed_dim)
+
+    def forward(self, stacked_emb):
+        x = stacked_emb.permute(1, 0, 2)
+        x = self.transformer(x)
+        combined_emb = x.mean(dim=0)
+        return self.output_layer(combined_emb)

decoder_state_dict.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bdaf828572883b42267344e5e2be29431ec225ecd6ac8d65db68e252120837fe
+size 33893202