diff --git a/skl2onnx/operator_converters/label_encoder.py b/skl2onnx/operator_converters/label_encoder.py
index e617fa607..9fdb18e84 100644
--- a/skl2onnx/operator_converters/label_encoder.py
+++ b/skl2onnx/operator_converters/label_encoder.py
@@ -4,7 +4,7 @@
 # license information.
 # --------------------------------------------------------------------------
 
-from ..common.data_types import StringTensorType, Int64TensorType
+import numpy as np
 from ..common._registration import register_converter
 
 
@@ -12,20 +12,18 @@ def convert_sklearn_label_encoder(scope, operator, container):
     op = operator.raw_operator
     op_type = 'LabelEncoder'
     attrs = {'name': scope.get_unique_operator_name(op_type)}
-    attrs['classes_strings'] = [str(c) for c in op.classes_]
-
-    if isinstance(operator.inputs[0].type, Int64TensorType):
-        attrs['default_int64'] = -1
-    elif isinstance(operator.inputs[0].type, StringTensorType):
-        attrs['default_string'] = '__unknown__'
+    classes = op.classes_
+    if np.issubdtype(classes.dtype, np.floating):
+        attrs['keys_floats'] = classes
+    elif np.issubdtype(classes.dtype, np.signedinteger):
+        attrs['keys_int64s'] = classes
     else:
-        raise RuntimeError(
-            'Unsupported input type: %s. It must be int64 or dtring.'
-            '' % type(operator.inputs[0].type))
+        attrs['keys_strings'] = np.array([s.encode('utf-8') for s in classes])
+    attrs['values_int64s'] = np.arange(len(classes))
 
     container.add_node(op_type, operator.input_full_names,
                        operator.output_full_names, op_domain='ai.onnx.ml',
-                       **attrs)
+                       op_version=2, **attrs)
 
 
 register_converter('SklearnLabelEncoder', convert_sklearn_label_encoder)
diff --git a/skl2onnx/shape_calculators/label_encoder.py b/skl2onnx/shape_calculators/label_encoder.py
index 8e20ce615..eda678435 100644
--- a/skl2onnx/shape_calculators/label_encoder.py
+++ b/skl2onnx/shape_calculators/label_encoder.py
@@ -6,6 +6,7 @@
 
 import copy
 from ..common._registration import register_shape_calculator
+from ..common.data_types import FloatTensorType
 from ..common.data_types import Int64TensorType, StringTensorType
 from ..common.utils import check_input_and_output_numbers
 from ..common.utils import check_input_and_output_types
@@ -18,7 +19,8 @@ def calculate_sklearn_label_encoder_output_shapes(operator):
     """
     check_input_and_output_numbers(operator, output_count_range=1)
     check_input_and_output_types(operator, good_input_types=[
-                                 Int64TensorType, StringTensorType])
+                                 FloatTensorType, Int64TensorType,
+                                 StringTensorType])
 
     input_shape = copy.deepcopy(operator.inputs[0].type.shape)
     operator.outputs[0].type = Int64TensorType(copy.deepcopy(input_shape))
diff --git a/tests/test_sklearn_label_encoder_converter.py b/tests/test_sklearn_label_encoder_converter.py
index 7069e2b16..840f48179 100644
--- a/tests/test_sklearn_label_encoder_converter.py
+++ b/tests/test_sklearn_label_encoder_converter.py
@@ -1,8 +1,7 @@
-"""
-Tests scikit-labebencoder converter.
-"""
+"""Tests scikit-LabelEncoder converter"""
+
 import unittest
-import numpy
+import numpy as np
 from sklearn.preprocessing import LabelEncoder
 from skl2onnx import convert_sklearn
 from skl2onnx.common.data_types import StringTensorType
@@ -22,10 +21,55 @@ def test_model_label_encoder(self):
         self.assertTrue(model_onnx is not None)
         self.assertTrue(model_onnx.graph.node is not None)
         dump_data_and_model(
-            numpy.array(data),
+            np.array(data),
             model,
             model_onnx,
             basename="SklearnLabelEncoder",
+            allow_failure="StrictVersion("
+            "onnxruntime.__version__)"
+            "<= StrictVersion('0.5.0')",
+        )
+
+    def test_model_label_encoder_float(self):
+        model = LabelEncoder()
+        data = np.array([1.2, 3.4, 5.4, 1.2])
+        model.fit(data)
+        model_onnx = convert_sklearn(
+            model,
+            "scikit-learn label encoder",
+            [("input", StringTensorType([1, 1]))],
+        )
+        self.assertTrue(model_onnx is not None)
+        self.assertTrue(model_onnx.graph.node is not None)
+        dump_data_and_model(
+            data,
+            model,
+            model_onnx,
+            basename="SklearnLabelEncoderFloat",
+            allow_failure="StrictVersion("
+            "onnxruntime.__version__)"
+            "<= StrictVersion('0.5.0')",
+        )
+
+    def test_model_label_encoder_int(self):
+        model = LabelEncoder()
+        data = np.array([10, 3, 5, -34, 0])
+        model.fit(data)
+        model_onnx = convert_sklearn(
+            model,
+            "scikit-learn label encoder",
+            [("input", StringTensorType([1, 1]))],
+        )
+        self.assertTrue(model_onnx is not None)
+        self.assertTrue(model_onnx.graph.node is not None)
+        dump_data_and_model(
+            data,
+            model,
+            model_onnx,
+            basename="SklearnLabelEncoderInt",
+            allow_failure="StrictVersion("
+            "onnxruntime.__version__)"
+            "<= StrictVersion('0.5.0')",
         )