In particular, RoBERTa was trained on a dataset of 160GB of text, which is more than 10 times larger than the dataset used to train BERT.
Additionally, RoBERTa uses a dynamic masking technique during training that helps the model learn more robust and generalizable representations of words..
RoBERTa is an extension of BERT with changes to the pretraining procedure.
The modifications include: training the model longer, with bigger batches, over more data. removing the next sentence prediction objective..
Moreover, rather than the 16GB dataset that was originally used to train BERT, RoBERTa is trained on a massive dataset that spans over 160GB of uncompressed text.
The dataset for RoBERTa contains (16GB) of English Wikipedia and Books Corpus, which are used in BERT..
Like BERT, RoBERTa is a transformer-based language model that uses self-attention to process input sequences and generate contextualized representations of words in a sentence.
One key difference between RoBERTa and BERT is that RoBERTa was trained on a much larger dataset and using a more effective training procedure..
RoBERTa is a reimplementation of BERT with some modifications to the key hyperparameters and minor embedding tweaks.
It uses a byte-level BPE as a tokenizer (similar to GPT-2) and a different pretraining scheme..
RoBERTa is trained on a massive dataset of over 160GB of uncompressed text instead of the 16GB dataset originally used to train BERT.
Moreover, RoBERTa is trained with i) FULL-SENTENCES without NSP loss, ii) dynamic masking, iii) large mini-batches, and iv) a larger byte-level BPE..
Model description
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion..
Model Architecture
RoBERTa uses a Byte-Level BPE tokenizer with a larger subword vocabulary (50k vs 32k).
RoBERTa implements dynamic word masking and drops next sentence prediction task..
