readme: ---
  language:
  
  - ru
  
  license: apache-2.0
  
  ---
  
  # Model DmitryPogrebnoy/MedRuBertTiny2
  
  # Model Description
  
  This model is fine-tuned version
  of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
  .
  The code for the fine-tuned process can be
  found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_rubert_tiny2/fine_tune_rubert_tiny2.py)
  .
  The model is fine-tuned on a specially collected dataset of over 30,000 medical anamneses in Russian.
  The collected dataset can be
  found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/data/anamnesis/processed/all_anamnesis.csv).
  
  This model was created as part of a master's project to develop a method for correcting typos
  in medical histories using BERT models as a ranking of candidates.
  The project is open source and can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker).
  
  # How to Get Started With the Model
  
  You can use the model directly with a pipeline for masked language modeling:
  
  ```python
  >>> from transformers import pipeline
  >>> pipeline = pipeline('fill-mask', model='DmitryPogrebnoy/MedRuBertTiny2')
  >>> pipeline("У пациента [MASK] боль в грудине.")
  [{'score': 0.4527082145214081,
    'token': 29626,
    'token_str': 'боль',
    'sequence': 'У пациента боль боль в грудине.'},
   {'score': 0.05768931284546852,
    'token': 46275,
    'token_str': 'головной',
    'sequence': 'У пациента головной боль в грудине.'},
   {'score': 0.02957102842628956,
    'token': 4674,
    'token_str': 'есть',
    'sequence': 'У пациента есть боль в грудине.'},
   {'score': 0.02168550342321396,
    'token': 10030,
    'token_str': 'нет',
    'sequence': 'У пациента нет боль в грудине.'},
   {'score': 0.02051634155213833,
    'token': 60730,
    'token_str': 'болит',
    'sequence': 'У пациента болит боль в грудине.'}]
  ```
  
  Or you can load the model and tokenizer and do what you need to do:
  
  ```python
  >>> from transformers import AutoTokenizer, AutoModelForMaskedLM
  >>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/MedRuBertTiny2")
  >>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedRuBertTiny2")
  ```
  
  
  