Update README.md

README.md

@@ -14,15 +14,16 @@ Model trained on a mixture of 7 denoisers like UL2 with several differences (htt
 
 It trained on Russian language corpus (300GB).   Dataset is the same as for ruT5 models. 
 
-Bbpe tokenizer. 
+Bbpe tokenizer. 50257 + special tokens 107. Prefix tokens: '<LM>','<SC1>'...'<SC6>'
 
 First half of the time model trained on the small part of all datasets (1%,3GB) and without prefixes in each task.
 
 For RSG we trained as described in the T5 paper. First, we trained multitask for all tasks. Then we took the best checkpoint for the task and trained it further.
+RSG submit here https://russiansuperglue.com/login/submit_info/1936
 
 Total training time was around 45 days on 112 A100 GPUs.
 
-Training loss:
+Training loss
 ![Screenshot 2023-01-21 at 11.36.52.png](https://s3.amazonaws.com/moonup/production/uploads/1674290304538-5f91b1208a61a359f44e1851.png)
 
 We continue to experiment...