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DmitryPogrebnoy/
MedMDebertaV3

Name: MedMDebertaV3
Availability: InStock

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Model MedMDebertaV3

Model Description

This model is fine-tuned version of microsoft/mdeberta-v3-base. 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/MedMDebertaV3')
>> > pipeline("У пациента [MASK] боль в грудине.")
[{'score': 0.05280596762895584, 
  'token': 4595, 
  'token_str': 'суд',
  'sequence': 'У пациента суд боль в грудине.'}, 
 {'score': 0.050577640533447266, 
  'token': 19157, 
  'token_str': 'времени', 
  'sequence': 'У пациента времени боль в грудине.'}, 
 {'score': 0.02754475176334381, 
  'token': 19174, 
  'token_str': 'препарат', 
  'sequence': 'У пациента препарат боль в грудине.'}, 
 {'score': 0.027341477572917938, 
  'token': 125009, 
  'token_str': 'рошен', 
  'sequence': 'У пациентарошен боль в грудине.'}, 
 {'score': 0.022251157090067863, 
  'token': 19441, 
  'token_str': 'енный', 
  'sequence': 'У пациентаенный 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/MedMDebertaV3")
>> > model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedMDebertaV3")