The RoBERTa model was pretrained on a dataset created by combining several sources including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. It uses a tokenization scheme with a vocabulary size of 50,000 and replaces 15% of the tokens with either a special masking token or a random token. The model achieved impressive results when fine-tuned on various downstream NLP tasks, outperforming its predecessor BERT in many areas.
The RoBERTa model was pretrained on a dataset created by combining several sources including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. It uses a tokenization scheme with a vocabulary size of 50,000 and replaces 15% of the tokens with either a special masking token or a random token. The model achieved impressive results when fine-tuned on various downstream NLP tasks, outperforming its predecessor BERT in many areas.
text prompt, should include exactly one <mask> token
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where is my father? (0.09)
where is my mother? (0.08)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English.
Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team.
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at a model like GPT2.
The RoBERTa model was pretrained on the reunion of five datasets:
Together these datasets weigh 160GB of text.
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
the model take pieces of 512 contiguous tokens that may span over documents. The beginning of a new document is marked
with <s>
and the end of one by </s>
The details of the masking procedure for each sentence are the following:
<mask>
.Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The optimizer used is Adam with a learning rate of 6e-4, \(\beta_{1} = 0.9\), \(\beta_{2} = 0.98\) and \(\epsilon = 1e-6\), a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learning rate after.
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
---|---|---|---|---|---|---|---|---|
87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 |
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}