A Family of Pretrained Transformer Language Models for Russian
This work addresses the problem of limited NLP resources for Russian language researchers and developers, though it is incremental as it applies existing methods to a new language.
The authors tackled the lack of attention to developing Transformer language models for Russian by introducing a collection of 13 specialized models across various architectures, and they reported evaluation results on Russian language understanding and generation benchmarks.
Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) architectures. We provide a report on the model architecture design and pretraining, and the results of evaluating their generalization abilities on Russian language understanding and generation datasets and benchmarks. By pretraining and releasing these specialized Transformer LMs, we aim to broaden the scope of the NLP research directions and enable the development of industrial solutions for the Russian language.