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Many datasets were composed for the first time, and a leaderboard of models for the Russian language with comparable results is also presented.Modern universal language models and transformers such as BERT, ELMo, XLNet, RoBERTa and others need to be properly compared and evaluated. The format of the GLUE benchmark is model-agnostic, so any system capable of processing sentence and sentence pairs and producing corresponding predictions is eligible to participate. Adhering to the GLUE and SuperGLUE methodology, we present a set of test tasks for general language understanding and leaderboard models. For the first time a complete test for Russian language was developed, which is similar to its English analog. A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set. SuperGLUE follows the basic design of GLUE: It consists of a public leaderboard built around eight language understanding tasks, drawing on existing data, accompanied by a single-number performance metric, and an analysis toolkit. RoBERTa currently ranks first on GLUE’s numerical score leaderboard with state-of-the-art performance on 4 of 9 GLUE tasks. Many datasets were composed for the first time, and a leaderboard of models for the Russian language with comparable results is also presented. SuperGLUE is available at this http URL. SuperGLUE is a new benchmark styled after original GLUE benchmark with a set of more difficult language understanding tasks, improved resources, and a new public leaderboard. In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. Adhering to the GLUE and SuperGLUE methodology, we present a set of test tasks for general language understanding and leaderboard models.For the first time a complete test for Russian language was developed, which is similar to its English analog. Comments: NeurIPS 2019, this http URL updating acknowledegments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:1905.00537 … We offer testing methodology based on tasks, typically proposed for “strong AI” — logic, commonsense, reasoning. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. “SuperGLUE comprises new ways to …