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The T5 model and its unified text-to-text paradigm contributed in advancingthe state-of-the-art for many natural language processing tasks. While somemultilingual variants of the T5 model have recently been introduced, theirperformances were found to provide suboptimal performances for languages otherthan English if compared to monolingual variants. We are motivated by thesefindings to introduce IT5, the first family of encoder-decoder transformermodels pretrained specifically on Italian. We perform a thorough cleaning of aweb-crawled Italian corpus including more than 40 billion words and use it topretrain three IT5 models of different sizes. The performance of IT5 models andtheir multilingual counterparts is then evaluated on a broad range of naturallanguage understanding and generation benchmarks for Italian. We find themonolingual IT5 models to provide the best scale-to-performance ratio acrosstested models, consistently outperforming their multilingual counterparts andsetting a new state-of-the-art for most Italian conditional language generationtasks.

IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation