update readme

README.md

@@ -89,15 +89,18 @@ BERT_MODEL_CLASSES = [BertModel, BertForPreTraining, BertForMaskedLM, BertForNex
                       BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
                       BertForQuestionAnswering]
 
-# All the classes for an architecture can be loaded from pretrained weights for this architecture
-# Note that additional weights added for fine-tuning are only initialized and need to be trained on the down-stream task
+# All the classes for an architecture can be initiated from pretrained weights for this architecture
+# Note that additional weights added for fine-tuning are only initialized
+# and need to be trained on the down-stream task
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 for model_class in BERT_MODEL_CLASSES:
     # Load pretrained model/tokenizer
     model = model_class.from_pretrained('bert-base-uncased')
 
 # Models can return full list of hidden-states & attentions weights at each layer
-model = model_class.from_pretrained(pretrained_weights, output_hidden_states=True, output_attentions=True)
+model = model_class.from_pretrained(pretrained_weights,
+                                    output_hidden_states=True,
+                                    output_attentions=True)
 input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")])
 all_hidden_states, all_attentions = model(input_ids)[-2:]