readme: ---
  language: en
  tags:
  - exbert
  license: mit
  datasets:
  - bookcorpus
  - wikipedia
  ---
  
  # 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.
  
  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.
  
  ## 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 the [model hub](https://huggingface.co/models?filter=roberta) 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 model like GPT2.
  
  ### How to use
  
  You can use this model directly with a pipeline for masked language modeling:
  
  ```python
  >>> from transformers import 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:
  
  ```python
  from transformers import RobertaTokenizer, RobertaModel
  tokenizer = 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:
  
  ```python
  from transformers import RobertaTokenizer, TFRobertaModel
  tokenizer = 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 from
  neutral. Therefore, the model can have biased predictions:
  
  ```python
  >>> from transformers import 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 <mas