Update modeling.py

modeling.py

@@ -1,51 +1,51 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import PreTrainedModel, AutoModel, AutoConfig
-
-
-class Encoder(nn.Module):
-    def __init__(self, base_encoder):
-        super().__init__()
-        self.encoder = base_encoder
-
-    def forward(self, inputs):
-        outputs = self.encoder(**inputs, output_hidden_states=True)
-        last_hidden = outputs.hidden_states[-1]
-        mask = inputs["attention_mask"].unsqueeze(-1).float()
-        pooled = (last_hidden * mask).sum(1) / mask.sum(1).clamp(min=1e-9)
-        return F.normalize(pooled, p=2, dim=1)
-
-
-class Classifier(nn.Module):
-    def __init__(self, input_dim=768, num_classes=28):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            nn.Linear(input_dim, 512),
-            nn.LayerNorm(512),
-            nn.GELU(),
-            nn.Dropout(0.25),
-            nn.Linear(512, num_classes),
-        )
-
-    def forward(self, x):
-        return self.mlp(x)
-
-
-class RobertaEmoPillars(PreTrainedModel):
-    config_class = AutoConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        base_encoder = AutoModel.from_pretrained(config._name_or_path)
-        self.encoder = Encoder(base_encoder)
-        self.classifier = Classifier(
-            input_dim=base_encoder.config.hidden_size,
-            num_classes=config.num_labels,
-        )
-
-    def forward(self, input_ids=None, attention_mask=None):
-        inputs = {"input_ids": input_ids, "attention_mask": attention_mask}
-        emb = self.encoder(inputs)
-        logits = self.classifier(emb)
-        return logits
+import torch
+import torch.nn as nn
+from transformers.modeling_outputs import SequenceClassifierOutput
+from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaModel
+
+
+class RobertaForSequenceClassification(RobertaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.roberta = RobertaModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Load weights
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        labels=None,
+        **kwargs
+    ):
+        outputs = self.roberta(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+        )
+
+        pooled_output = outputs[1]  # CLS token
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                loss_fct = nn.MSELoss()
+                loss = loss_fct(logits.squeeze(), labels.squeeze())
+            else:
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels.float())
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )