Update README.md

README.md

@@ -1,3 +1,61 @@
 ---
-license: bsd-3-clause
+language:
+- ru
+license: apache-2.0
 ---
+
+# Model DmitryPogrebnoy/distilbert-base-russian-cased
+
+# Model Description
+
+This model is russian version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased).
+The code for the transforming process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/distilbert_base_russian_cased/distilbert_from_multilang_to_ru.ipynb).
+
+This model give exactly the same representations produced by the original model which preserves the original accuracy.
+There is a similar model of [Geotrend/distilbert-base-ru-cased](https://huggingface.co/Geotrend/distilbert-base-ru-cased). 
+However, our model is derived from a slightly different approach. 
+Instead of using wikipedia's Russian dataset to pick the necessary tokens, 
+we used regular expressions in this model to select only Russian tokens, punctuation marks, numbers and other service tokens. 
+Thus, our model contains several hundred tokens, which have been filtered out in [Geotrend/distilbert-base-ru-cased](https://huggingface.co/Geotrend/distilbert-base-ru-cased).
+
+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/distilbert-base-russian-cased')
+>>> pipeline("Я [MASK] на заводе.")
+[{'score': 0.11498937010765076,
+  'token': 1709,
+  'token_str': 'работал',
+  'sequence': 'Я работал на заводе.'},
+ {'score': 0.07212855666875839,
+  'token': 12375,
+  'token_str': '##росла',
+  'sequence': 'Яросла на заводе.'},
+ {'score': 0.03575785085558891,
+  'token': 4059,
+  'token_str': 'находился',
+  'sequence': 'Я находился на заводе.'},
+ {'score': 0.02496381290256977,
+  'token': 5075,
+  'token_str': 'работает',
+  'sequence': 'Я работает на заводе.'},
+ {'score': 0.020675526931881905,
+  'token': 5774,
+  'token_str': '##дро',
+  'sequence': 'Ядро на заводе.'}]
+```
+
+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/distilbert-base-russian-cased")
+>>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/distilbert-base-russian-cased")
+```