fix(executor): delete some useless codes

jina/executors/encoders/nlp/transformer.py

@@ -70,9 +70,9 @@ def post_init(self):
         if self.model_name in ('bert-base-uncased', 'distilbert-base-cased', 'roberta-base', 'xlm-roberta-base'):
             self.cls_pos = 'head'
         elif self.model_name in ('xlnet-base-cased'):
-            self.tokenizer.pad_token = '<PAD>'
             self.cls_pos = 'tail'
-        elif self.model_name in ('openai-gpt', 'gpt2', 'xlm-mlm-enfr-1024'):
+
+        if self.model_name in ('openai-gpt', 'gpt2', 'xlm-mlm-enfr-1024', 'xlnet-base-cased'):
             self.tokenizer.pad_token = '<PAD>'
 
     def encode(self, data: 'np.ndarray', *args, **kwargs) -> 'np.ndarray':
@@ -169,7 +169,7 @@ def post_init(self):
         import tensorflow as tf
         from transformers import TFBertModel, TFOpenAIGPTModel, TFGPT2Model, TFXLNetModel, TFXLMModel, \
             TFDistilBertModel, TFRobertaModel, TFXLMRobertaModel
-        tf_model_dict = {
+        model_dict = {
             'bert-base-uncased': TFBertModel,
             'openai-gpt': TFOpenAIGPTModel,
             'gpt2': TFGPT2Model,
@@ -179,18 +179,16 @@ def post_init(self):
             'roberta-base': TFRobertaModel,
             'xlm-roberta-base': TFXLMRobertaModel,
         }
-        self.model = tf_model_dict[self.model_name].from_pretrained(self._tmp_path)
+        self.model = model_dict[self.model_name].from_pretrained(self._tmp_path)
         self._tensor_func = tf.constant
         self._sess_func = tf.GradientTape
 
-        if self.model_name in ('xlnet-base-cased'):
-            self.model.resize_token_embeddings(len(self.tokenizer))
-        elif self.model_name in ('openai-gpt', 'gpt2', 'xlm-mlm-enfr-1024'):
+        if self.model_name in ('xlnet-base-cased', 'openai-gpt', 'gpt2', 'xlm-mlm-enfr-1024'):
             self.model.resize_token_embeddings(len(self.tokenizer))
 
 class TransformerTorchEncoder(TransformerEncoder):
     """
-    Internally, TransformerTFEncoder wraps the pytorch-version of transformers from huggingface.
+    Internally, TransformerTorchEncoder wraps the pytorch-version of transformers from huggingface.
     """
     def post_init(self):
         super().post_init()
@@ -213,7 +211,5 @@ def post_init(self):
         self._tensor_func = torch.tensor
         self._sess_func = torch.no_grad
 
-        if self.model_name in ('xlnet-base-cased'):
-            self.model.resize_token_embeddings(len(self.tokenizer))
-        elif self.model_name in ('openai-gpt', 'gpt2', 'xlm-mlm-enfr-1024'):
+        if self.model_name in ('xlnet-base-cased', 'openai-gpt', 'gpt2', 'xlm-mlm-enfr-1024'):
             self.model.resize_token_embeddings(len(self.tokenizer))