diff --git a/jina/executors/encoders/nlp/transformer.py b/jina/executors/encoders/nlp/transformer.py
index 95cd5614e80c5..5ec29e7f8a720 100644
--- a/jina/executors/encoders/nlp/transformer.py
+++ b/jina/executors/encoders/nlp/transformer.py
@@ -57,42 +57,42 @@ def post_init(self):
             raise ValueError
 
         try:
-            import tensorflow as tf
-            from transformers import TFBertModel, TFOpenAIGPTModel, TFGPT2Model, TFXLNetModel, TFXLMModel, \
-                TFDistilBertModel, TFRobertaModel, TFXLMRobertaModel
-            tf_model_dict = {
-                'bert-base-uncased': TFBertModel,
-                'openai-gpt': TFOpenAIGPTModel,
-                'gpt2': TFGPT2Model,
-                'xlnet-base-cased': TFXLNetModel,
-                'xlm-mlm-enfr-1024': TFXLMModel,
-                'distilbert-base-cased': TFDistilBertModel,
-                'roberta-base': TFRobertaModel,
-                'xlm-roberta-base': TFXLMRobertaModel,
+            import torch
+            from transformers import BertModel, OpenAIGPTModel, GPT2Model, XLNetModel, XLMModel, DistilBertModel, \
+                RobertaModel, XLMRobertaModel
+
+            model_dict = {
+                'bert-base-uncased': BertModel,
+                'openai-gpt': OpenAIGPTModel,
+                'gpt2': GPT2Model,
+                'xlnet-base-cased': XLNetModel,
+                'xlm-mlm-enfr-1024': XLMModel,
+                'distilbert-base-cased': DistilBertModel,
+                'roberta-base': RobertaModel,
+                'xlm-roberta-base': XLMRobertaModel,
             }
-            model_class = tf_model_dict[self.model_name]
-            self._tensor_func = tf.constant
-            self._sess_func = tf.GradientTape
+            model_class = model_dict[self.model_name]
+            self._tensor_func = torch.tensor
+            self._sess_func = torch.no_grad
 
         except:
             try:
-                import torch
-                from transformers import BertModel, OpenAIGPTModel, GPT2Model, XLNetModel, XLMModel, DistilBertModel, \
-                    RobertaModel, XLMRobertaModel
-
-                model_dict = {
-                    'bert-base-uncased': BertModel,
-                    'openai-gpt': OpenAIGPTModel,
-                    'gpt2': GPT2Model,
-                    'xlnet-base-cased': XLNetModel,
-                    'xlm-mlm-enfr-1024': XLMModel,
-                    'distilbert-base-cased': DistilBertModel,
-                    'roberta-base': RobertaModel,
-                    'xlm-roberta-base': XLMRobertaModel,
+                import tensorflow as tf
+                from transformers import TFBertModel, TFOpenAIGPTModel, TFGPT2Model, TFXLNetModel, TFXLMModel, \
+                    TFDistilBertModel, TFRobertaModel, TFXLMRobertaModel
+                tf_model_dict = {
+                    'bert-base-uncased': TFBertModel,
+                    'openai-gpt': TFOpenAIGPTModel,
+                    'gpt2': TFGPT2Model,
+                    'xlnet-base-cased': TFXLNetModel,
+                    'xlm-mlm-enfr-1024': TFXLMModel,
+                    'distilbert-base-cased': TFDistilBertModel,
+                    'roberta-base': TFRobertaModel,
+                    'xlm-roberta-base': TFXLMRobertaModel,
                 }
-                model_class = model_dict[self.model_name]
-                self._tensor_func = torch.tensor
-                self._sess_func = torch.no_grad
+                model_class = tf_model_dict[self.model_name]
+                self._tensor_func = tf.constant
+                self._sess_func = tf.GradientTape
             except:
                 raise ModuleNotFoundError('Tensorflow or Pytorch is required!')