Example snippet for BertForQuestionAnswering

src/transformers/modeling_bert.py

@@ -1392,8 +1392,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
         question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
-        input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]"
-        input_ids = tokenizer.encode(input_text)
+        input_ids = tokenizer.encode(question, text)
         token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))]
         start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids]))
         all_tokens = tokenizer.convert_ids_to_tokens(input_ids)