Create models/deberta_model.py

models/deberta_model.py

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+
+import torch
+import torch.nn as nn
+from transformers import DebertaModel
+from config import DROPOUT_RATE, DEBERTA_MODEL_NAME
+
+class DebertaMultiOutputModel(nn.Module):
+    tokenizer_name = DEBERTA_MODEL_NAME
+
+    def __init__(self, num_labels):
+        super(DebertaMultiOutputModel, self).__init__()
+        self.deberta = DebertaModel.from_pretrained(DEBERTA_MODEL_NAME)
+        self.dropout = nn.Dropout(DROPOUT_RATE)
+        self.classifiers = nn.ModuleList([
+            nn.Linear(self.deberta.config.hidden_size, n_classes) for n_classes in num_labels
+        ])
+
+    def forward(self, input_ids, attention_mask):
+        last_hidden_state = self.deberta(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
+        pooled_output = last_hidden_state[:, 0]  # [CLS] token representation
+        pooled_output = self.dropout(pooled_output)
+        return [classifier(pooled_output) for classifier in self.classifiers]