Update README.md

README.md

@@ -1,3 +1,66 @@
 ---
+language:
+
+- ru
+
 license: apache-2.0
+
 ---
+
+# Model DmitryPogrebnoy/MedRuBertTiny2
+
+# Model Description
+
+This model is fine-tuned version
+of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
+.
+The code for the fine-tuned process can be
+found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_rubert_tiny2/fine_tune_rubert_tiny2.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/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")
+```
+
+