 Italian BERT  The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the [OPUS corpora](http://opus.nlpl.eu/) collection. The final training corpus has a size of 13GB and 2,050,057,573 tokens.  For sentence splitting, we use NLTK (faster compared to spacy). Our cased and uncased models are training with an initial sequence length of 512 subwords for ~2-3M steps.  For the XXL Italian models, we use the same training data from OPUS and extend it with data from the Italian part of the [OSCAR corpus](https://traces1.inria.fr/oscar/). Thus, the final training corpus has a size of 81GB and 13,138,379,147 tokens.  Note: Unfortunately, a wrong vocab size was used when training the XXL models. This explains the mismatch of the "real" vocab size of 31102, compared to the vocab size specified in `config.json`. However, the model is working and all evaluations were done under those circumstances. See [this issue](https://github.com/dbmdz/berts/issues/7) for more information.  The Italian ELECTRA model was trained on the "XXL" corpus for 1M steps in total using a batch size of 128. We pretty much following the ELECTRA training procedure as used for [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master/electra).  