diff --git a/pytext/data/tensorizers.py b/pytext/data/tensorizers.py
index ce3e10ab2..e07168e8b 100644
--- a/pytext/data/tensorizers.py
+++ b/pytext/data/tensorizers.py
@@ -2,6 +2,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 
 import json
+import re
 from typing import List, Optional, Tuple, Type
 
 import torch
@@ -226,10 +227,6 @@ def tensorize(self, batch):
         return (pad_and_tensorize(characters), pad_and_tensorize(lengths))
 
 
-class FloatVectorTensorizer(Tensorizer):
-    """TODO: support for dense features."""
-
-
 class LabelTensorizer(Tensorizer):
     """Numberize labels."""
 
@@ -329,7 +326,7 @@ def __init__(self, column: str):
         self.column = column
 
     def numberize(self, row):
-        res = json.loads(row[self.column].replace(" ", ","))
+        res = json.loads(re.sub(r",? +", ",", row[self.column]))
         if type(res) is not list:
             raise ValueError(f"{res} is not a valid float list")
         return [float(n) for n in res]
diff --git a/pytext/data/test/tensorizers_test.py b/pytext/data/test/tensorizers_test.py
index c738e65b7..a56ae0c05 100644
--- a/pytext/data/test/tensorizers_test.py
+++ b/pytext/data/test/tensorizers_test.py
@@ -9,6 +9,7 @@
 from pytext.data.tensorizers import (
     ByteTensorizer,
     CharacterTokenTensorizer,
+    FloatListTensorizer,
     LabelTensorizer,
     TokenTensorizer,
     initialize_tensorizers,
@@ -138,3 +139,17 @@ def test_create_label_tensors(self):
         self.assertEqual(1, tensor)
         with self.assertRaises(Exception):
             tensor = next(tensors)
+
+    def test_create_float_list_tensor(self):
+        tensorizer = FloatListTensorizer(column="dense")
+        rows = [
+            {"dense": "[0.1,0.2]"},  # comma
+            {"dense": "[0.1, 0.2]"},  # comma with single space
+            {"dense": "[0.1,  0.2]"},  # comma with multiple spaces
+            {"dense": "[0.1 0.2]"},  # space
+            {"dense": "[0.1  0.2]"},  # multiple spaces
+        ]
+
+        tensors = (tensorizer.numberize(row) for row in rows)
+        for tensor in tensors:
+            self.assertEqual([0.1, 0.2], tensor)