Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
+language:
+- ru
 license: apache-2.0
 ---
+
+# Model DmitryPogrebnoy/MedDistilBertBaseRuCased
+
+# Model Description
+
+This model is fine-tuned version of [DmitryPogrebnoy/distilbert-base-russian-cased](https://huggingface.co/DmitryPogrebnoy/distilbert-base-russian-cased). 
+The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_distilbert_base_russian_cased/fine_tune_distilbert_base_russian_cased.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/MedDistilBertBaseRuCased')
+>>> pipeline("У пациента [MASK] боль в грудине.")
+[{'score': 0.1733243614435196,
+  'token': 6880,
+  'token_str': 'имеется',
+  'sequence': 'У пациента имеется боль в грудине.'},
+ {'score': 0.08818087726831436,
+  'token': 1433,
+  'token_str': 'есть',
+  'sequence': 'У пациента есть боль в грудине.'},
+ {'score': 0.03620537742972374,
+  'token': 3793,
+  'token_str': 'особенно',
+  'sequence': 'У пациента особенно боль в грудине.'},
+ {'score': 0.03438418731093407,
+  'token': 5168,
+  'token_str': 'бол',
+  'sequence': 'У пациента бол боль в грудине.'},
+ {'score': 0.032936397939920425,
+  'token': 6281,
+  'token_str': 'протекает',
+  'sequence': 'У пациента протекает 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/MedDistilBertBaseRuCased")
+>>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedDistilBertBaseRuCased")
+```
+
+