Upload modeling_roberta_sentiment.py

modeling_roberta_sentiment.py

@@ -0,0 +1,86 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class TransformerBlock(nn.Module):
+    def __init__(self, hidden_dim, num_heads, ffn_dim, dropout):
+        super().__init__()
+        self.attn_norm = nn.LayerNorm(hidden_dim)
+        self.ffn_norm = nn.LayerNorm(hidden_dim)
+        self.attn = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout, batch_first=True)
+        self.ffn = nn.Sequential(
+            nn.Linear(hidden_dim, ffn_dim),
+            nn.GELU(),
+            nn.Linear(ffn_dim, hidden_dim),
+            nn.Dropout(dropout)
+        )
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, attention_mask):
+        batch_size, seq_len, _ = x.size()
+    
+        # No transpose needed since batch_first=True
+        x_norm = self.attn_norm(x)
+        attn_mask = (1 - attention_mask).bool()  # [batch_size, seq_len]
+    
+        assert attn_mask.shape == (batch_size, seq_len), \
+            f"Expected {batch_size=} and {seq_len=}, got {attn_mask.shape}"
+    
+        # Run self-attention (no transpose)
+        attn_out, _ = self.attn(
+            x_norm, x_norm, x_norm,
+            key_padding_mask=attn_mask
+        )
+    
+        # Residual + FF
+        x = x + self.dropout(attn_out)
+        x_norm = self.ffn_norm(x)
+        x = x + self.dropout(self.ffn(x_norm))
+    
+        return x
+
+class RobertaForSentimentClassification(nn.Module):
+    def __init__(self, vocab_size, max_len=128, num_classes=5):
+        super().__init__()
+        self.hidden_size = 512
+        self.max_len = max_len
+        self.num_heads = 8
+        self.ffn_dim = 2048
+        self.num_layers = 6
+        self.dropout_rate = 0.1
+
+        # Embeddings
+        self.token_emb = nn.Embedding(vocab_size, self.hidden_size)
+        self.position_emb = nn.Embedding(max_len, self.hidden_size)
+        self.dropout = nn.Dropout(self.dropout_rate)
+
+        # Transformer blocks
+        self.layers = nn.ModuleList([
+            TransformerBlock(self.hidden_size, self.num_heads, self.ffn_dim, self.dropout_rate)
+            for _ in range(self.num_layers)
+        ])
+
+        # Classification head
+        self.classifier = nn.Sequential(
+            nn.Linear(self.hidden_size, self.hidden_size),
+            nn.GELU(),
+            nn.Dropout(self.dropout_rate),
+            nn.Linear(self.hidden_size, num_classes)
+        )
+
+    def forward(self, input_ids, attention_mask):
+        batch_size, seq_len = input_ids.size()
+
+        # Embeddings
+        positions = torch.arange(0, seq_len, device=input_ids.device).unsqueeze(0).expand(batch_size, seq_len)
+        x = self.token_emb(input_ids) + self.position_emb(positions)
+        x = self.dropout(x)
+
+        # Transformer blocks
+        for layer in self.layers:
+            x = layer(x, attention_mask)
+
+        # Use <s> token (first position) for classification
+        cls_token = x[:, 0]  # shape: (batch_size, hidden_size)
+        logits = self.classifier(cls_token)
+        return logits