language:
- ru
license: apache-2.0
FRED-T5 large 800M (Full-scale Russian Enhanced Denoisers T5)
Architecture based on T5.
It has 24 layers and 1024 hidden size. More details in config.json.
The model trained on a mixture of 7 denoisers like UL2 with several differences (https://arxiv.org/abs/2205.05131).
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 dataset (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/2060
Total training time was around 35 days on 160 V100 GPUs.
We'll release checkpoint to the public soon.