Model MedRuRobertaLarge
Model Description
This model is fine-tuned version of ruRoberta-large. 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:
>>> from transformers import pipeline
>>> pipeline = pipeline('fill-mask', model='DmitryPogrebnoy/MedRuRobertaLarge')
>>> pipeline("У пациента <mask> боль в грудине.")
[{'score': 0.2467374950647354,
'token': 9233,
'token_str': ' сильный',
'sequence': 'У пациента сильный боль в грудине.'},
{'score': 0.16476310789585114,
'token': 27876,
'token_str': ' постоянный',
'sequence': 'У пациента постоянный боль в грудине.'},
{'score': 0.07211139053106308,
'token': 19551,
'token_str': ' острый',
'sequence': 'У пациента острый боль в грудине.'},
{'score': 0.0616639070212841,
'token': 18840,
'token_str': ' сильная',
'sequence': 'У пациента сильная боль в грудине.'},
{'score': 0.029712719842791557,
'token': 40176,
'token_str': ' острая',
'sequence': 'У пациента острая боль в грудине.'}]
Or you can load the model and tokenizer and do what you need to do:
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge")
>>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge")