
# Model MedRuRobertaLarge

# Model Description

This model is fine-tuned version of [ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large). 
The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_ru_roberta_large/fine_tune_ru_roberta_large.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/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:

```python
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge")
>>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge")
```
{"language": ["ru"], "license": "apache-2.0"}
transformers pytorch roberta fill-mask ru license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# 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: 



Or you can load the model and tokenizer and do what you need to do:


# 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: 



Or you can load the model and tokenizer and do what you need to do:
TAGS
#transformers #pytorch #roberta #fill-mask #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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: 



Or you can load the model and tokenizer and do what you need to do:
passage: TAGS
#transformers #pytorch #roberta #fill-mask #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# 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: 



Or you can load the model and tokenizer and do what you need to do: