diff --git a/pliers/extractors/models.py b/pliers/extractors/models.py
index ed7cb7a2..672e32d5 100644
--- a/pliers/extractors/models.py
+++ b/pliers/extractors/models.py
@@ -51,6 +51,9 @@ class TFHubExtractor(Extractor):
             output for compatibility with extractor result
         transform_inp (optional): function to transform Stim.data 
             for compatibility with model input format
+        output_key (str): key to desired in output in
+            dictionary. Set to None if the output is not a dictionary, 
+            or to output all keys in dictionary.
         keras_kwargs (dict): arguments to hub.KerasLayer call
     '''
 
@@ -59,12 +62,12 @@ class TFHubExtractor(Extractor):
 
     def __init__(self, url_or_path, features=None,
                  transform_out=None, transform_inp=None,
-                 keras_kwargs=None):
+                 output_key=None, keras_kwargs=None):
         verify_dependencies(['tensorflow_hub'])
         if keras_kwargs is None:
             keras_kwargs = {}
         self.keras_kwargs = keras_kwargs
-
+        self.output_key = output_key
         self.model = hub.KerasLayer(url_or_path, **keras_kwargs)
 
         self.url_or_path = url_or_path
@@ -74,11 +77,18 @@ def __init__(self, url_or_path, features=None,
         super().__init__()
 
     def get_feature_names(self, out):
+        # Manual feature names always take precedence
         if self.features:
             return listify(self.features)
+        # Infer feature names from output
         else:
+            # If dict, use provided output key, or all keys
             if isinstance(out, dict):
-                return list(out.keys())
+                if self.output_key:
+                    return [self.output_key]
+                else:
+                    return list(out.keys())
+            # Worst case, use generic feature names
             else:
                 return ['feature_' + str(i) 
                         for i in range(out.shape[-1])]
@@ -93,10 +103,27 @@ def _preprocess(self, stim):
                 return stim.data
 
     def _postprocess(self, out):
+        # If key is provided, return only that key
+        if self.output_key:
+            try:
+                out =  out[self.output_key]
+            except KeyError:
+                raise ValueError(f'{self.output_key} is not a valid key.'
+                                'Check which keys are available in the output '
+                                'at the model URL ({self.url_or_path})')
+            except (IndexError, TypeError):
+                raise ValueError(f'Model output is not a dictionary. '
+                                  'Try initialize the extractor with output_key=None.')
+
+        # If output is a dict and no output key, return all keys
+        if isinstance(out, dict):
+            out = np.vstack(list(out.values())).T
+
+        # Always squeeze last dimension if it is 1
+        out = out.numpy().squeeze()
+
         if self.transform_out:
             out = self.transform_out(out)
-        if not isinstance(out, np.ndarray):
-            out = out.numpy().squeeze()
         return out
 
     def _get_timing(self, out, stim):
@@ -116,12 +143,7 @@ def _get_timing(self, out, stim):
     def _extract(self, stim):
         inp = self._preprocess(stim)
         out = self.model(inp)
-
         features = self.get_feature_names(out)
-
-        if isinstance(out, dict):
-            out = np.vstack(list(out.values())).T
-
         out = self._postprocess(out)
                 
         onsets, durations, orders = self._get_timing(out, stim)
@@ -232,7 +254,11 @@ class TFHubTextExtractor(TFHubExtractor):
             The number of items must  match the number of features 
             in the model output. For example, if a text encoder 
             outputting 768-dimensional encoding is passed 
-            (e.g. base BERT), this must be a list containing 768 items. 
+                 output_key (str): key to desired embedding in output 
+            dictionary (see documentation at 
+            https://www.tensorflow.org/hub/common_saved_model_apis/text).
+            Set to None is the output is not a dictionary, or to
+            output all keys   (e.g. base BERT), this must be a list containing 768 items. 
             Each dimension in the model output will be returned as a 
             separate feature in the ExtractorResult.
             Alternatively, the model output can be packed into a single 
@@ -246,7 +272,8 @@ class TFHubTextExtractor(TFHubExtractor):
         output_key (str): key to desired embedding in output 
             dictionary (see documentation at 
             https://www.tensorflow.org/hub/common_saved_model_apis/text).
-            Set to None is the output is not a dictionary.
+            Set to None is the output is not a dictionary, or to
+            output all keys
         preprocessor_url_or_path (str): if the model requires 
             preprocessing through another TFHub model, specifies the 
             url or path to the preprocessing module. Information on 
@@ -266,7 +293,7 @@ def __init__(self,
                  preprocessor_kwargs=None,
                  keras_kwargs=None,
                  **kwargs):
-        super().__init__(url_or_path, features, 
+        super().__init__(url_or_path, features, output_key=output_key,
                          keras_kwargs=keras_kwargs, 
                          **kwargs)
         self.output_key = output_key
@@ -287,22 +314,6 @@ def _preprocess(self, stim):
                 self.preprocessor_url_or_path, **self.preprocessor_kwargs)
             x = preprocessor(x)
         return x
-    
-    def _postprocess(self, out):
-        if not self.output_key:
-            return out.numpy().squeeze()
-        else:
-            try:
-                return out[self.output_key].numpy().squeeze()
-            except KeyError:
-                raise ValueError(f'{self.output_key} is not a valid key.'
-                                'Check which keys are available in the output '
-                                'embedding dictionary in TFHub docs '
-                                '(https://www.tensorflow.org/hub/common_saved_model_apis/text)'
-                                f' or at the model URL ({self.url_or_path})')
-            except (IndexError, TypeError):
-                raise ValueError(f'Model output is not a dictionary. '
-                                  'Try initialize the extractor with output_key=None.')
 
 
 class TensorFlowKerasApplicationExtractor(ImageExtractor):