Update README.md

README.md

@@ -1,3 +1,55 @@
 ---
-license: gpl-3.0
+language:
+- ru
+license: apache-2.0
 ---
+
+# Model MedRuRobertaLarge
+
+# Model Description
+
+This model is fine-tuned version of [ruRoberta-large](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
+>>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge")
+>>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge")
+```