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DmitryPogrebnoy/
MedRuBertTiny2

Name: MedRuBertTiny2
Availability: InStock

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Model DmitryPogrebnoy/MedRuBertTiny2

Model Description

This model is fine-tuned version of cointegrated/rubert-tiny2 . The code for the fine-tuned process can be found here . 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.

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.

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")