training (Devlin et al., 2019), which includes a careful evaluation of the effects of hyperparmeter tuning and training set size. To improve the training procedure, RoBERTa removes the Next Sentence Prediction (NSP) task from BERT’s pre-training and introduces dynamic masking so that the masked token changes during the training epochs. RoBERTa uses BookCorpus (16G), CC-NEWS (76G), OpenWebText (38G) and Stories (31G) data while BERT only uses BookCorpus as training data only. The magic is an improved recipe for training BERT models. Larger batch-training sizes were also found to be more useful in the training procedure. RoBERTa builds on BERT’s language masking strategy and modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. However, unicode characters can account for a sizeable portion of this vocabulary when modeling large and diverse corpora, such as the ones considered in this work. Authors: Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. The cleaned CommonCrawl data that it is trained on takes up a whopping 2.5tb of storage! Training … Instead of full words, BPE relies on subwords units, which are extracted by performing statistical analysis of the training corpus. Download PDF Abstract: Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Original BERT was trained on a combination of BookCorpus plus English Wikipedia, which totals 16GB of uncompressed text. The biggest update that XLM-Roberta offers over the original is a significantly increased amount of training data. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer amount of time. RoBERTa, which was implemented in PyTorch, modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. The modifications are simple, they include: (1) Training the model longer, with bigger batches, over more data. Importantly, RoBERTa uses 160 GB of text for pre-training, including 16GB of Books Corpus and English Wikipedia used in BERT. Importantly, RoBERTa uses 160 GB of text for pre-training, including 16GB of Books Corpus … We ﬁnd that BERT was signiﬁcantly undertrained and propose an im-proved recipe for training BERT models, which we call RoBERTa, that can match or exceed the performance of all of the post-BERT methods. It is several orders of magnitude larger than the Wiki-100 corpus that was used to train its predecessor and the scale-up is particularly noticeable in the lower resourced languages. The additional data included CommonCrawl News dataset (63 million articles, 76 GB), Web text corpus (38 GB) and Stories from Common Crawl (31 GB). Static Masking vs Dynamic Masking
BPE vocabulary sizes typically range from 10K-100K subword units. Title: RoBERTa: A Robustly Optimized BERT Pretraining Approach.