Update README.md

README.md

@@ -10,15 +10,15 @@ Architecture based on T5.
 
 It has 24 layers and 1536 hidden size. More details in config.json.
 
-Model trained on a mixture of 7 denoisers like UL2 with several differences (https://arxiv.org/abs/2205.05131).
+The model trained on a mixture of 7 denoisers like UL2 with several differences (https://arxiv.org/abs/2205.05131).
 
-It trained on Russian language corpus (300GB).   Dataset is the same as for ruT5 models. 
+It was trained on Russian language corpus (300GB).   The dataset is the same as for ruT5 models. 
 
 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 tasks.
+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.
+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.