Create evaluate.py

evaluate.py

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+import torch
+from daedalus_mobile import DaedalusMobile
+from tokenizer import DaedalusTokenizer
+from config import config
+
+def evaluate(model, device, eval_loader):
+    model.eval()
+    total_loss = 0
+    with torch.no_grad():
+        for batch in eval_loader:
+            input_ids, attention_mask, labels = batch
+            input_ids, attention_mask, labels = input_ids.to(device), attention_mask.to(device), labels.to(device)
+            loss = model.eval_step((input_ids, attention_mask, labels))
+            total_loss += loss.item()
+    return total_loss / len(eval_loader)
+
+def main():
+    device = torch.device(config.device)
+    model = DaedalusMobile(config)
+    model.to(device)
+    tokenizer = DaedalusTokenizer(config)
+    eval_loader = torch.utils.data.DataLoader(dataset=eval_dataset, batch_size=config.batch_size, shuffle=False)
+    loss = evaluate(model, device, eval_loader)
+    print(f'Loss: {loss:.4f}')
+
+if __name__ == '__main__':
+    main()