readme: fine-tuned bert-base-chinese for intent recognition task on [dataset](https://huggingface.co/datasets/nlp-guild/intent-recognition-biomedical)
  
  # Usage
  ```python
  
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  from transformers import TextClassificationPipeline
  
  tokenizer = AutoTokenizer.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical")
  model = AutoModelForSequenceClassification.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical")
  nlp = TextClassificationPipeline(model = model, tokenizer = tokenizer)
  
  label_set = [
  '定义',
  '病因',
  '预防',
  '临床表现(病症表现)',
  '相关病症',
  '治疗方法',
  '所属科室',
  '传染性',
  '治愈率',
  '禁忌',
  '化验/体检方案',
  '治疗时间',
  '其他'
  ]
  
  def readable_results(top_k:int, usr_query:str):
      raw = nlp(usr_query, top_k = top_k)
      def f(x):
          index = int(x['label'][6:])
          x['label'] = label_set[index]
      
      for i in raw:
          f(i)
      return raw
   
   readable_results(3,'得了心脏病怎么办')
  
  '''
  [{'label': '治疗方法', 'score': 0.9994503855705261},
   {'label': '其他', 'score': 0.00018375989748165011},
   {'label': '临床表现(病症表现)', 'score': 0.00010841596667887643}]
  '''
  ```