readme: ---
  language:
  - ru
  license: apache-2.0
  ---
  
  # Model DmitryPogrebnoy/MedDistilBertBaseRuCased
  
  # Model Description
  
  This model is fine-tuned version of [DmitryPogrebnoy/distilbert-base-russian-cased](https://huggingface.co/DmitryPogrebnoy/distilbert-base-russian-cased). 
  The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_distilbert_base_russian_cased/fine_tune_distilbert_base_russian_cased.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/MedDistilBertBaseRuCased')
  >>> pipeline("У пациента [MASK] боль в грудине.")
  [{'score': 0.1733243614435196,
    'token': 6880,
    'token_str': 'имеется',
    'sequence': 'У пациента имеется боль в грудине.'},
   {'score': 0.08818087726831436,
    'token': 1433,
    'token_str': 'есть',
    'sequence': 'У пациента есть боль в грудине.'},
   {'score': 0.03620537742972374,
    'token': 3793,
    'token_str': 'особенно',
    'sequence': 'У пациента особенно боль в грудине.'},
   {'score': 0.03438418731093407,
    'token': 5168,
    'token_str': 'бол',
    'sequence': 'У пациента бол боль в грудине.'},
   {'score': 0.032936397939920425,
    'token': 6281,
    'token_str': 'протекает',
    'sequence': 'У пациента протекает 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/MedDistilBertBaseRuCased")
  >>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedDistilBertBaseRuCased")
  ```
  
  
  