readme: ---
  license: bigscience-bloom-rail-1.0
  ---
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  # RoBERTa base model
  
  Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
  [this paper](https://arxiv.org/abs/1907.11692) and first released in
  [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). 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.
  
  ## Model description
  
  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.
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  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.
  
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