readme: ---
  license: apache-2.0
  language: zh
  tags:
  - Token Classification
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  ---
  
  ## Model description
  
  This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M dataset including 60K+ authorized medical articles. We proposed to randomly confuse 30% sentences of these articles by adding noise with a either visually or phonologically resembled characters. Consequently, the fine-tuned model can achieve 96% accuracy on our test dataset. 
  
  ## Intended uses & limitations
  
  You can use this model directly with a pipeline for token classification:
  ```python
  >>> from transformers import (AutoModelForTokenClassification, AutoTokenizer)
  >>> from transformers import pipeline
  
  >>> hub_model_id = "9pinus/macbert-base-chinese-medical-collation"
  
  >>> model = AutoModelForTokenClassification.from_pretrained(hub_model_id)
  >>> tokenizer = AutoTokenizer.from_pretrained(hub_model_id)
  >>> classifier = pipeline('ner', model=model, tokenizer=tokenizer)
  >>> result = classifier("如果病情较重，可适当口服甲肖唑片、环酯红霉素片等药物进行抗感染镇痛。")
  
  >>> for item in result:
  >>>     if item['entity'] == 1:
  >>>         print(item)
  
  {'entity': 1, 'score': 0.58127016, 'index': 14, 'word': '肖', 'start': 13, 'end': 14}
  
  ```
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  