Update README

README.md

@@ -17,12 +17,13 @@ The model is used for extractive question answering. You can download the model
 You can use the model directly with a pipeline for extractive question answering:
 
 ```python
->>> from transformers import pipeline
->>> path = 'uer/roberta-base-chinese-extractive-qa'
->>> nlp = pipeline('question-answering', model=path, tokenizer=path)
->>> QA_input = {'question': "小王在哪上学？",'context': "小王在北京上学，他今年二十岁。"}
->>> nlp(QA_input)
-    {'score': 0.7618623375892639, 'start': 3, 'end': 5, 'answer': '北京'}
+>>> from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline
+>>> model = AutoModelForQuestionAnswering.from_pretrained('uer/roberta-base-chinese-extractive-qa')
+>>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-chinese-extractive-qa')
+>>> QA = pipeline('question-answering', model=model, tokenizer=tokenizer)
+>>> QA_input = {'question': "著名诗歌《假如生活欺骗了你》的作者是",'context': "普希金从那里学习人民的语言，吸取了许多有益的养料，这一切对普希金后来的创作产生了很大的影响。这两年里，普希金创作了不少优秀的作品，如《囚徒》、《致大海》、《致凯恩》和《假如生活欺骗了你》等几十首抒情诗，叙事诗《努林伯爵》，历史剧《鲍里斯·戈都诺夫》，以及《叶甫盖尼·奥涅金》前六章。"}
+>>> QA(QA_input)
+    {'score': 0.9766426682472229, 'start': 0, 'end': 3, 'answer': '普希金'}
 ```
 
 ## Training data