fix: enwik dataset fix

CNN-model/dataset_loaders/EnWik9.py

@@ -1,25 +1,43 @@
 from datasets import load_dataset
+from torch.utils.data import Dataset
+import torch
 from os.path import curdir, join
-from .Dataset import Dataset
-from torch.utils.data import TensorDataset
 from typing import Callable
 
 
 class EnWik9DataSet(Dataset):
-    def __init__(self, root: str = "data", transform: Callable = None):
+    def __init__(self, root: str = "data", transform: Callable | None = None):
-        super().__init__(root, transform)
+        super().__init__()
+        self.transform = transform
 
+        # HuggingFace dataset: string text
         path = join(curdir, root)
-        self._root = path
-
         data = load_dataset("haukur/enwik9", cache_dir=path, split="train")
+
+        # Extract raw text
         text = data["text"]
-        self.dataset = TensorDataset(text)
+
+        # Convert text (Python string) → bytes → tensor of ints 0–255
+        # UTF-8 but non-ASCII bytes may exceed 255, so enforce modulo or ignore errors
+        byte_data = "".join(text).encode("utf-8", errors="replace")
+        self.data = torch.tensor(list(byte_data), dtype=torch.long)
+
+        # Model uses fixed 128-length context
+        self.context_length = 128
 
     def __len__(self):
-        return len(self.dataset)
+        # number of sliding windows
+        return len(self.data) - self.context_length
 
     def __getitem__(self, idx):
-        if self.transform is not None:
+        # context window
-            return self.transform(self.dataset[idx])
+        x = self.data[idx : idx + self.context_length]
-        return self.dataset[idx]
+
+        # next byte target
+        y = self.data[idx + self.context_length]
+
+        if self.transform:
+            x = self.transform(x)
+
+        return x, y
+

CNN-model/main_cnn.py

@@ -20,6 +20,7 @@ if __name__ == "__main__":
     parser.add_argument("--model-path", type=str, required=False)
     args = parser.parse_args()
 
+    print("Loading in the dataset...")
     if args.method == "train":
         dataset: Dataset = EnWik9DataSet(transform=lambda x: x.to(DEVICE))
     elif args.method == "optuna":
@@ -28,9 +29,11 @@ if __name__ == "__main__":
         raise ValueError(f"Unknown method: {args.method}")
 
     dataset_length = len(dataset)
+    print(f"Dataset size = {dataset_length}")
+
     training_size = ceil(0.8 * dataset_length)
 
-    print(f"training set size = {training_size}, validation set size {dataset_length - training_size}")
+    print(f"Training set size = {training_size}, Validation set size {dataset_length - training_size}")
 
     train_set, validate_set = torch.utils.data.random_split(dataset,
                                                             [training_size, dataset_length - training_size])
@@ -40,6 +43,7 @@ if __name__ == "__main__":
 
     model = None
     if args.model_path is not None:
+        print("Loading the model...")
         model = torch.load(args.model_path)
 
     trainer: Trainer = OptunaTrainer() if args.method == "optuna" else FullTrainer()