diff --git a/src/modalities/dataloader/dataset.py b/src/modalities/dataloader/dataset.py
index 1cc05625..7c953e71 100644
--- a/src/modalities/dataloader/dataset.py
+++ b/src/modalities/dataloader/dataset.py
@@ -11,11 +11,10 @@
 from tqdm import tqdm
 from transformers import BatchEncoding
 
+from modalities.dataloader.create_packed_data import EmbeddedStreamData
+from modalities.dataloader.large_file_lines_reader import LargeFileLinesReader
 from modalities.tokenization.tokenizer_wrapper import TokenizerWrapper
 
-from ..dataloader.large_file_lines_reader import LargeFileLinesReader
-from .create_packed_data import EmbeddedStreamData
-
 
 class Dataset(TorchdataSet):
     """Dataset class."""
@@ -411,9 +410,9 @@ def _generate_packing_index(self) -> list[tuple[int, int]]:
 class CombinedDataset(Dataset):
     """Combines multiple datasets into one large dataset at runtime.
 
-    Note: When using this class to combine multiple `PackedMemMapDataset`s, then each packed sample
+    Note: When using this class to combine multiple `PackedMemMapDataset`s, each packed sample
     is packed from a single dataset (i.e., the samples are not mixed between datasets).
-    In the Dataloader a batch will still contain packed samples from different datasets.
+    In the Dataloader, a batch will still contain packed samples from different datasets.
     """
 
     def __init__(self, datasets: list[Dataset]):
@@ -423,29 +422,13 @@ def __init__(self, datasets: list[Dataset]):
             datasets (list[Dataset]): A list of datasets to combine.
         """
         self.datasets = datasets
-        self.cumulative_sizes = CombinedDataset._get_cumulated_sizes(datasets=datasets)
-
-    @staticmethod
-    def _get_cumulated_sizes(datasets: list[Dataset]) -> list[int]:
-        total = 0
-        cumulated_sizes = [0]
-        for dataset in datasets:
-            total += len(dataset)
-            cumulated_sizes.append(total)
-        return cumulated_sizes
-
-    def _find_dataset_idx(self, idx: int) -> int:
-        for i, cumulative_size in enumerate(self.cumulative_sizes):
-            if idx < cumulative_size:
-                return i - 1
-        raise IndexError(f"Index {idx} is out of bounds.")
+        self.cumulative_sizes = np.cumsum([len(ds) for ds in datasets], dtype=np.int64)
 
     def __len__(self) -> int:
         return self.cumulative_sizes[-1]
 
     def __getitem__(self, idx: int) -> dict:
-        dataset_idx = self._find_dataset_idx(idx)
-        local_idx = idx - self.cumulative_sizes[dataset_idx]
+        dataset_idx = np.searchsorted(self.cumulative_sizes, idx, side="right")
+        local_idx = idx - (self.cumulative_sizes[dataset_idx - 1] if dataset_idx > 0 else 0)
 
-        sample = self.datasets[dataset_idx][local_idx]
-        return sample
+        return self.datasets[dataset_idx][local_idx]