diff --git a/dataframe-hasktorch.cabal b/dataframe-hasktorch.cabal
--- a/dataframe-hasktorch.cabal
+++ b/dataframe-hasktorch.cabal
@@ -1,6 +1,6 @@
 cabal-version:      3.0
 name:               dataframe-hasktorch
-version:            0.2.0.2
+version:            0.4.0.0
 synopsis:           Converts between dataframes and hasktorch tensors
 
 description:        
@@ -36,14 +36,10 @@
     exposed-modules:  DataFrame.Hasktorch
 
     build-depends:    base >= 4.11 && < 5,
-                      vector ^>= 0.13,
-                      dataframe-core ^>= 1.1,
-                      dataframe-operations ^>= 1.1.1,
+                      vector >= 0.13 && < 0.15,
+                      dataframe-core ^>= 2.1,
+                      dataframe-operations ^>= 2.1,
                       hasktorch >= 0.2.1.6 && < 0.3
-    if impl(ghc >= 9.12)
-      build-depends: ghc-typelits-natnormalise == 0.9.3
-    else
-      build-depends: ghc-typelits-natnormalise >= 0.7.7 && < 0.9
 
     hs-source-dirs:   src
 
@@ -56,5 +52,4 @@
     hs-source-dirs:   test
     main-is:          Main.hs
     build-depends:
-        base >= 4.11 && < 5,
-        dataframe-hasktorch
+        base >= 4.11 && < 5
diff --git a/src/DataFrame/Hasktorch.hs b/src/DataFrame/Hasktorch.hs
--- a/src/DataFrame/Hasktorch.hs
+++ b/src/DataFrame/Hasktorch.hs
@@ -11,41 +11,15 @@
 import qualified DataFrame.Operations.Core as D
 
 import Control.Exception (throw)
-import DataFrame.Internal.DataFrame (DataFrame)
+import DataFrame.Core (DataFrame)
 import Torch
 
-{- | Converts a dataframe to a floating-point tensor.
-
-This function converts all columns in the dataframe to floats and creates
-a tensor suitable for machine learning operations. The tensor dimensions
-are determined by the dataframe's shape.
-
-==== __Dimensional behavior__
-
-* Multi-column dataframe: Creates a 2D tensor with shape @[rows, columns]@
-* Single-column dataframe: Creates a 1D tensor with shape @[rows]@
-
-==== __Conversion process__
-
-1. Converts the dataframe to a float matrix using 'D.toFloatMatrix'
-2. Flattens the matrix features into a 1D representation
-3. Reshapes into the appropriate tensor dimensions
-
-==== __Throws__
-
-* 'DataFrameException' - if any column cannot be converted to float
-
-==== __Examples__
+{- | Convert a dataframe to a floating-point tensor of shape @[rows, columns]@
+(or @[rows]@ when single-column). Throws 'DataFrameException' if a column
+cannot be converted to float.
 
 >>> toTensor df  -- where df has shape (100, 5)
 Tensor with shape [100, 5]
-
->>> toTensor df  -- where df has shape (100, 1)
-Tensor with shape [100]
-
-==== __See also__
-
-* 'toIntTensor' - for integer tensor conversion
 -}
 toTensor :: DataFrame -> Tensor
 toTensor df = case D.toFloatMatrix df of
@@ -57,43 +31,12 @@
          in
             reshape dims' (asTensor (flattenFeatures m))
 
-{- | Converts a dataframe to an integer tensor.
-
-This function converts all columns in the dataframe to integers and creates
-a tensor suitable for machine learning operations (e.g., classification labels,
-discrete features). The tensor dimensions are determined by the dataframe's shape.
-
-==== __Dimensional behavior__
-
-* Multi-column dataframe: Creates a 2D tensor with shape @[rows, columns]@
-* Single-column dataframe: Creates a 1D tensor with shape @[rows]@
-
-==== __Conversion process__
-
-1. Converts the dataframe to an int matrix using 'D.toIntMatrix'
-2. Flattens the matrix features into a 1D representation
-3. Reshapes into the appropriate tensor dimensions
-
-==== __Throws__
-
-* 'DataFrameException' - if any column cannot be converted to int
-
-==== __Examples__
-
->>> toIntTensor labelsDf  -- where labelsDf has shape (100, 1)
-Tensor with shape [100]
+{- | Convert a dataframe to an integer tensor of shape @[rows, columns]@ (or
+@[rows]@ when single-column). Floating-point values are rounded. Throws
+'DataFrameException' if a column cannot be converted to int.
 
 >>> toIntTensor featuresDf  -- where featuresDf has shape (100, 3)
 Tensor with shape [100, 3]
-
-==== __Note__
-
-Floating-point values in the dataframe will be rounded to the nearest integer.
-See 'D.toIntMatrix' for details on the conversion behavior.
-
-==== __See also__
-
-* 'toTensor' - for floating-point tensor conversion
 -}
 toIntTensor :: DataFrame -> Tensor
 toIntTensor df = case D.toIntMatrix df of