RoBERTa base model

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced inthis paper and first released inthis repository. This model is case-sensitive: itmakes 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 bythe Hugging Face team.

Model description

RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This meansit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots ofpublicly 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 modelrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predictthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words oneafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model tolearn 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 featuresuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standardclassifier using the features produced by the BERT model as inputs.

Intended uses & limitations

You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.See themodel hub to look for fine-tuned versions on a task thatinterests 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 textgeneration you should look at a model like GPT2.

How to use

You can use this model directly with a pipeline for masked language modeling:

>>>from transformersimport pipeline>>>unmasker = pipeline('fill-mask', model='roberta-base')>>>unmasker("Hello I'm a <mask> model.")[{'sequence':"<s>Hello I'm a male model.</s>",'score':0.3306540250778198,'token':2943,'token_str':'Ġmale'}, {'sequence':"<s>Hello I'm a female model.</s>",'score':0.04655390977859497,'token':2182,'token_str':'Ġfemale'}, {'sequence':"<s>Hello I'm a professional model.</s>",'score':0.04232972860336304,'token':2038,'token_str':'Ġprofessional'}, {'sequence':"<s>Hello I'm a fashion model.</s>",'score':0.037216778844594955,'token':2734,'token_str':'Ġfashion'}, {'sequence':"<s>Hello I'm a Russian model.</s>",'score':0.03253649175167084,'token':1083,'token_str':'ĠRussian'}]

Here is how to use this model to get the features of a given text in PyTorch:

from transformersimport RobertaTokenizer, RobertaModeltokenizer = RobertaTokenizer.from_pretrained('roberta-base')model = RobertaModel.from_pretrained('roberta-base')text ="Replace me by any text you'd like."encoded_input = tokenizer(text, return_tensors='pt')output = model(**encoded_input)

and in TensorFlow:

from transformersimport RobertaTokenizer, TFRobertaModeltokenizer = RobertaTokenizer.from_pretrained('roberta-base')model = TFRobertaModel.from_pretrained('roberta-base')text ="Replace me by any text you'd like."encoded_input = tokenizer(text, return_tensors='tf')output = model(encoded_input)

Limitations and bias

The training data used for this model contains a lot of unfiltered content from the internet, which is far fromneutral. Therefore, the model can have biased predictions:

>>>from transformersimport pipeline>>>unmasker = pipeline('fill-mask', model='roberta-base')>>>unmasker("The man worked as a <mask>.")[{'sequence':'<s>The man worked as a mechanic.</s>','score':0.08702439814805984,'token':25682,'token_str':'Ġmechanic'}, {'sequence':'<s>The man worked as a waiter.</s>','score':0.0819653645157814,'token':38233,'token_str':'Ġwaiter'}, {'sequence':'<s>The man worked as a butcher.</s>','score':0.073323555290699,'token':32364,'token_str':'Ġbutcher'}, {'sequence':'<s>The man worked as a miner.</s>','score':0.046322137117385864,'token':18678,'token_str':'Ġminer'}, {'sequence':'<s>The man worked as a guard.</s>','score':0.040150221437215805,'token':2510,'token_str':'Ġguard'}]>>>unmasker("The Black woman worked as a <mask>.")[{'sequence':'<s>The Black woman worked as a waitress.</s>','score':0.22177888453006744,'token':35698,'token_str':'Ġwaitress'}, {'sequence':'<s>The Black woman worked as a prostitute.</s>','score':0.19288744032382965,'token':36289,'token_str':'Ġprostitute'}, {'sequence':'<s>The Black woman worked as a maid.</s>','score':0.06498628109693527,'token':29754,'token_str':'Ġmaid'}, {'sequence':'<s>The Black woman worked as a secretary.</s>','score':0.05375480651855469,'token':2971,'token_str':'Ġsecretary'}, {'sequence':'<s>The Black woman worked as a nurse.</s>','score':0.05245552211999893,'token':9008,'token_str':'Ġnurse'}]

This bias will also affect all fine-tuned versions of this model.

Training data

The RoBERTa model was pretrained on the reunion of five datasets:

• BookCorpus, a dataset consisting of 11,038 unpublished books;
• English Wikipedia (excluding lists, tables and headers) ;
• CC-News, a dataset containing 63 millions English newsarticles crawled between September 2016 and February 2019.
• OpenWebText, an opensource recreation of the WebText dataset used totrain GPT-2,
• Stories a dataset containing a subset of CommonCrawl data filtered to match thestory-like style of Winograd schemas.

Together these datasets weigh 160GB of text.

Training procedure

Preprocessing

The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs ofthe model take pieces of 512 contiguous tokens that may span over documents. The beginning of a new document is markedwith<s> and the end of one by</s>

The details of the masking procedure for each sentence are the following:

• 15% of the tokens are masked.
• In 80% of the cases, the masked tokens are replaced by<mask>.
• In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
• In the 10% remaining cases, the masked tokens are left as is.

Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).

Pretraining

The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. Theoptimizer used is Adam with a learning rate of 6e-4,${\beta }_1=0.9\backslash beta\_\{1\}\; =\; 0.9$β1​=0.9,${\beta }_2=0.98\backslash beta\_\{2\}\; =\; 0.98$β2​=0.98 and$ϵ=1e-6\backslash epsilon\; =\; 1e-6$ϵ=1e−6, a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learningrate after.

Evaluation results

When fine-tuned on downstream tasks, this model achieves the following results:

Glue test results:

BibTeX entry and citation info

@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}}