Model description:

FRED-T5 1.7B (Full-scale Russian Enhanced Denoisers T5) Architecture based on T5. It has 24 layers and hidden size 1536. The model trained on a mixture of 7 denoisers like UL2 with several differences. It was trained on a Russian language corpus (300GB). The dataset is the same as for ruT5 models. Bbpe tokenizer. First half of the time model was trained on the small part of all datasets (1%,3GB) and without prefixes in each task. For RSG, we trained the model as described in the T5 paper. First, we trained to multitask for all rsg tasks. Then we took the best checkpoint for the task and trained it further. Model card – https://huggingface.co/sberbank-ai/FRED-T5-1.7B

Parameter description: