diff --git a/CHANGELOG.md b/CHANGELOG.md
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,10 @@
 # Revision history for dataframe-hasktorch
 
+## 0.1.0.1
+
+* Export `toIntTensor` function that converts a dataframe to an Int tensor.
+* `toTensor` now does automatic type conversion. `Nothing` is turned into `NaN` and other numeric types are changed to `Float` (be careful of precision errors).
+
 ## 0.1.0.0
 
 * Export `toTensor` function that converts a dataframe to a tensor.
diff --git a/dataframe-hasktorch.cabal b/dataframe-hasktorch.cabal
--- a/dataframe-hasktorch.cabal
+++ b/dataframe-hasktorch.cabal
@@ -1,9 +1,21 @@
 cabal-version:      3.0
 name:               dataframe-hasktorch
-version:            0.1.0.0
+version:            0.1.0.1
 synopsis:           Converts between dataframes and hasktorch tensors
 
-description:
+description:        
+    This package provides seamless conversion between dataframes and hasktorch tensors,
+    bridging the gap between data manipulation and machine learning workflows.
+    .
+    Key features:
+    .
+    * Convert dataframes to floating-point or integer tensors for ML training
+    * Automatic handling of multi-column and single-column dataframes
+    * Smart dimensional handling (1D tensors for single columns, 2D for multiple)
+    * Type-safe conversions with comprehensive error handling
+    .
+    Typical workflow: load and transform data using dataframes, then convert to
+    tensors for training neural networks with hasktorch.
 
 license:            MIT
 license-file:       LICENSE
@@ -25,7 +37,7 @@
 
     build-depends:    base >= 4.11 && < 5,
                       vector ^>= 0.13,
-                      dataframe >= 0.3.3.1 && < 0.6,
+                      dataframe >= 0.3.3.3 && < 0.6,
                       hasktorch >= 0.2.1.6 && < 0.3
 
     hs-source-dirs:   src
diff --git a/src/DataFrame/Hasktorch.hs b/src/DataFrame/Hasktorch.hs
--- a/src/DataFrame/Hasktorch.hs
+++ b/src/DataFrame/Hasktorch.hs
@@ -2,6 +2,7 @@
 
 module DataFrame.Hasktorch (
     toTensor,
+    toIntTensor,
 ) where
 
 import qualified Data.Vector as V
@@ -13,8 +14,41 @@
 import DataFrame (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__
+
+>>> 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.toMatrix df of
+toTensor df = case D.toFloatMatrix df of
     Left e -> throw e
     Right m ->
         let
@@ -23,7 +57,55 @@
          in
             reshape dims' (asTensor (flattenFeatures m))
 
-flattenFeatures :: V.Vector (VU.Vector Float) -> VU.Vector Float
+{- | 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]
+
+>>> 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
+    Left e -> throw e
+    Right m ->
+        let
+            (r, c) = D.dimensions df
+            dims' = if c == 1 then [r] else [r, c]
+         in
+            reshape dims' (asTensor (flattenFeatures m))
+
+flattenFeatures :: V.Vector (VU.Vector a) -> VU.Vector a
 flattenFeatures rows =
     let
         total = V.foldl' (\s v -> s + VU.length v) 0 rows