Create mcqa_bert.py

mcqa_bert.py

@@ -0,0 +1,26 @@
+# mcqa_bert.py
+# --------------------------------------------------
+# Plain BertModel + single‑unit classification head
+# --------------------------------------------------
+import torch
+import torch.nn as nn
+from transformers import BertModel
+
+
+class MCQABERT(nn.Module):
+    def __init__(self, ckpt: str = "bert-base-uncased"):
+        super().__init__()
+        self.encoder = BertModel.from_pretrained(ckpt)
+        self.head    = nn.Linear(self.encoder.config.hidden_size, 1)
+
+    # --------------------------------------------------
+
+    def forward(self, input_ids, attention_mask):
+        out   = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            return_dict=True,
+        )
+        cls_vec = out.last_hidden_state[:, 0]          # [CLS]
+        logits  = self.head(cls_vec).squeeze(-1)       # (B)
+        return logits