diff --git a/src/uni2ts/data/indexer/_base.py b/src/uni2ts/data/indexer/_base.py
index e326463..5675293 100644
--- a/src/uni2ts/data/indexer/_base.py
+++ b/src/uni2ts/data/indexer/_base.py
@@ -22,10 +22,27 @@
 
 
 class Indexer(abc.ABC, Sequence):
+    """
+    Base class for all Indexers.
+
+    An Indexer is responsible for extracting data from an underlying file format.
+    """
+
     def __init__(self, uniform: bool = False):
+        """
+        :param uniform: whether the underlying data has uniform length
+        """
         self.uniform = uniform
 
     def check_index(self, idx: int | slice | Iterable[int]):
+        """
+        Check the validity of a given index.
+
+        :param idx: index to check
+        :return: None
+        :raises IndexError: if idx is out of bounds
+        :raises NotImplementedError: if idx is not a valid type
+        """
         if isinstance(idx, int):
             if idx < 0 or idx >= len(self):
                 raise IndexError(f"Index {idx} out of bounds for length {len(self)}")
@@ -48,6 +65,12 @@ def check_index(self, idx: int | slice | Iterable[int]):
     def __getitem__(
         self, idx: int | slice | Iterable[int]
     ) -> dict[str, Data | BatchedData]:
+        """
+        Retrive the data from the underlying storage in dictionary format.
+
+        :param idx: index to retrieve
+        :return: underlying data with given index
+        """
         self.check_index(idx)
 
         if isinstance(idx, int):
@@ -72,9 +95,20 @@ def _getitem_int(self, idx: int) -> dict[str, Data]: ...
     def _getitem_iterable(self, idx: Iterable[int]) -> dict[str, BatchedData]: ...
 
     def get_uniform_probabilities(self) -> np.ndarray:
+        """
+        Obtains uniform probability distribution over all time series.
+
+        :return: uniform probability distribution
+        """
         return np.ones(len(self)) / len(self)
 
     def get_proportional_probabilities(self, field: str = "target") -> np.ndarray:
+        """
+        Obtain proportion of each time series based on number of time steps.
+
+        :param field: field name to measure time series length
+        :return: proportional probabilities
+        """
         if self.uniform:
             return self.get_uniform_probabilities()
 
diff --git a/src/uni2ts/data/indexer/hf_dataset_indexer.py b/src/uni2ts/data/indexer/hf_dataset_indexer.py
index a7f5d07..487ad6f 100644
--- a/src/uni2ts/data/indexer/hf_dataset_indexer.py
+++ b/src/uni2ts/data/indexer/hf_dataset_indexer.py
@@ -28,7 +28,15 @@
 
 
 class HuggingFaceDatasetIndexer(Indexer):
+    """
+    Indexer for Hugging Face Datasets
+    """
+
     def __init__(self, dataset: Dataset, uniform: bool = False):
+        """
+        :param dataset: underlying Hugging Face Dataset
+        :param uniform: whether the underlying data has uniform length
+        """
         super().__init__(uniform=uniform)
         self.dataset = dataset
         self.features = dict(self.dataset.features)
@@ -109,6 +117,14 @@ def _pa_column_to_numpy(
         return array
 
     def get_proportional_probabilities(self, field: str = "target") -> np.ndarray:
+        """
+        Obtain proportion of each time series based on number of time steps.
+        Leverages pyarrow.compute for fast implementation.
+
+        :param field: field name to measure time series length
+        :return: proportional probabilities
+        """
+
         if self.uniform:
             return self.get_uniform_probabilities()