Model almost complete, need to work on loss functions soon

src/model.py

@@ -24,13 +24,40 @@ class Model(nn.Module):
         self.feature_head = nn.Linear(hidden_size, 2)
         self.aspect_head = nn.Linear(hidden_size, 6)
         self.aspect_sentiment_head = nn.Linear(hidden_size, 3)
+
+    # Pass through encoder then extract the token representation
+    # Apply droupout to it, take scores for each head, return them in a dictionary
     def forward(self, input_ids, attention_mask):
         outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        output = outputs.last_hidden_state[:, 0, :]
 
+        output = self.dropout(output)
 
+        # Logits for each head:
+        bug_logits = self.bug_head(output)
+        feature_logits = self.feature_head(output)
+        aspect_logits = self.aspect_head(output)
+        aspect_sentiment = self.aspect_sentiment_head(output)
+        return {
+            'bug_report': bug_logits,
+            'feature_request': feature_logits,
+            'aspect': aspect_logits,
+            'aspect_sentiment': aspect_sentiment
+        }
     
-
+if __name__ == "__main__":
-
+    from dataset import ReviewDataset
+    from transformers import AutoTokenizer
+    from torch.utils.data import DataLoader
 
     tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
-model = AutoModelForMaskedLM.from_pretrained("FacebookAI/xlm-roberta-base")
+    dataset = ReviewDataset("data/processed/original_train.csv", tokenizer)
+    loader = DataLoader(dataset, batch_size=2)
+
+    batch = next(iter(loader))
+
+    model = Model()
+    outputs = model(batch["input_ids"], batch["attention_mask"])
+
+    for k, v in outputs.items():
+        print(k, v.shape)