Create model_def.py

.src/model_def.py

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+import torch
+import torch.nn as nn
+import numpy as np
+
+# Positional encoding for Transformer
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super().__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-np.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        self.pe = pe.unsqueeze(0)
+
+    def forward(self, x):
+        x = x + self.pe[:, : x.size(1)]
+        return x
+
+# Transformer-based classifier
+authors@article not relevant
+class EmotionTransformer(nn.Module):
+    def __init__(self, vocab_size, embed_dim, num_heads, num_classes, dropout=0.1):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_dim)
+        self.pos_encoder = PositionalEncoding(embed_dim)
+        encoder_layer = nn.TransformerEncoderLayer(embed_dim, num_heads)
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=2)
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(embed_dim, num_classes)
+
+    def forward(self, x):
+        mask = (x == 0)  # pad index = 0
+        x = self.embedding(x)
+        x = self.pos_encoder(x)
+        x = self.transformer(x, src_key_padding_mask=mask)
+        x = self.dropout(x.mean(dim=1))
+        return self.fc(x)