readme: Fine-tuned [Bert-Base-Chinese](https://huggingface.co/bert-base-chinese) for NER task on [Adapting/chinese_biomedical_NER_dataset](https://huggingface.co/datasets/Adapting/chinese_biomedical_NER_dataset)
  
  # Usage
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  tokenizer = AutoTokenizer.from_pretrained("Adapting/bert-base-chinese-finetuned-NER-biomedical")
  model = AutoModelForTokenClassification.from_pretrained("Adapting/bert-base-chinese-finetuned-NER-biomedical",revision='7f63e3d18b1dc3cc23041a89e77be21860704d2e')
  
  from transformers import pipeline
  nlp = pipeline('ner',model=model,tokenizer = tokenizer)
  
  tag_set = [
   'B_手术',
   'I_疾病和诊断',
   'B_症状',
   'I_解剖部位',
   'I_药物',
   'B_影像检查',
   'B_药物',
   'B_疾病和诊断',
   'I_影像检查',
   'I_手术',
   'B_解剖部位',
   'O',
   'B_实验室检验',
   'I_症状',
   'I_实验室检验'
   ]
   
  tag2id = lambda tag: tag_set.index(tag)
  id2tag = lambda id: tag_set[id]
  
  def readable_result(result):
  
      results_in_word = []
      j = 0
      while j < len(result):   
          i = result[j]
          entity = id2tag(int(i['entity'][i['entity'].index('_')+1:]))
          token = i['word']
          if entity.startswith('B'):
              entity_name = entity[entity.index('_')+1:]
  
              word = token
              j = j+1
              while j<len(result):
                  next = result[j]
                  next_ent = id2tag(int(next['entity'][next['entity'].index('_')+1:]))
                  next_token = next['word']
  
                  if next_ent.startswith('I') and next_ent[next_ent.index('_')+1:] == entity_name:
                      word += next_token
                      j += 1
  
                      if j >= len(result):
                          results_in_word.append((entity_name,word))
                  else:
                      results_in_word.append((entity_name,word))
                      break
  
          else:
              j += 1
      
      return results_in_word
  
  
          
  print(readable_result(nlp('淋球菌性尿道炎会引起头痛')))
  
  '''
  [('疾病和诊断', '淋球菌性尿道炎'), ('症状', '头痛')]
  '''
  ```