| ---
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| language: en
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| tags:
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| - exbert
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| license: mit
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| datasets:
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| - bookcorpus
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| - wikipedia
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| ---
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|
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| # RoBERTa large model
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|
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| Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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| [this paper](https://arxiv.org/abs/1907.11692) and first released in
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| [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
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| makes a difference between english and English.
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|
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| Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by
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| the Hugging Face team.
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|
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| ## Model description
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|
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| RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means
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| it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
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| publicly available data) with an automatic process to generate inputs and labels from those texts.
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|
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| More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
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| randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
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| the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
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| after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
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| learn a bidirectional representation of the sentence.
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|
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| This way, the model learns an inner representation of the English language that can then be used to extract features
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| useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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| classifier using the features produced by the BERT model as inputs.
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|
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| ## Intended uses & limitations
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|
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| You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
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| See the [model hub](https://huggingface.co/models?filter=roberta) to look for fine-tuned versions on a task that
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| interests you.
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| Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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| to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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| generation you should look at model like GPT2.
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|
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| ### How to use
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|
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| You can use this model directly with a pipeline for masked language modeling:
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|
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| ```python
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| >>> from transformers import pipeline
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| >>> unmasker = pipeline('fill-mask', model='roberta-large')
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| >>> unmasker("Hello I'm a <mask> model.")
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|
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| [{'sequence': "<s>Hello I'm a male model.</s>",
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| 'score': 0.3317350447177887,
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| 'token': 2943,
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| 'token_str': 'Ġmale'},
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| {'sequence': "<s>Hello I'm a fashion model.</s>",
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| 'score': 0.14171843230724335,
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| 'token': 2734,
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| 'token_str': 'Ġfashion'},
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| {'sequence': "<s>Hello I'm a professional model.</s>",
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| 'score': 0.04291723668575287,
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| 'token': 2038,
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| 'token_str': 'Ġprofessional'},
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| {'sequence': "<s>Hello I'm a freelance model.</s>",
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| 'score': 0.02134818211197853,
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| 'token': 18150,
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| 'token_str': 'Ġfreelance'},
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| {'sequence': "<s>Hello I'm a young model.</s>",
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| 'score': 0.021098261699080467,
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| 'token': 664,
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| 'token_str': 'Ġyoung'}]
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| ```
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| Here is how to use this model to get the features of a given text in PyTorch:
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|
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| ```python
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| from transformers import RobertaTokenizer, RobertaModel
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| tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
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| model = RobertaModel.from_pretrained('roberta-large')
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| text = "Replace me by any text you'd like."
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| encoded_input = tokenizer(text, return_tensors='pt')
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| output = model(**encoded_input)
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| ```
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|
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| and in TensorFlow:
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| ```python
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| from transformers import RobertaTokenizer, TFRobertaModel
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| tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
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| model = TFRobertaModel.from_pretrained('roberta-large')
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| text = "Replace me by any text you'd like."
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| encoded_input = tokenizer(text, return_tensors='tf')
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| output = model(encoded_input)
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| ```
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|
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| ### Limitations and bias
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| The training data used for this model contains a lot of unfiltered content from the internet, which is far from
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| neutral. Therefore, the model can have biased predictions:
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|
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| ```python
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| >>> from transformers import pipeline
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| >>> unmasker = pipeline('fill-mask', model='roberta-large')
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| >>> unmasker("The man worked as a <mask>.")
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|
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| [{'sequence': '<s>The man worked as a mechanic.</s>',
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| 'score': 0.08260300755500793,
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| 'token': 25682,
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| 'token_str': 'Ġmechanic'},
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| {'sequence': '<s>The man worked as a driver.</s>',
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| 'score': 0.05736079439520836,
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| 'token': 1393,
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| 'token_str': 'Ġdriver'},
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| {'sequence': '<s>The man worked as a teacher.</s>',
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| 'score': 0.04709019884467125,
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| 'token': 3254,
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| 'token_str': 'Ġteacher'},
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| {'sequence': '<s>The man worked as a bartender.</s>',
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| 'score': 0.04641604796051979,
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| 'token': 33080,
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| 'token_str': 'Ġbartender'},
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| {'sequence': '<s>The man worked as a waiter.</s>',
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| 'score': 0.04239227622747421,
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| 'token': 38233,
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| 'token_str': 'Ġwaiter'}]
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|
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| >>> unmasker("The woman worked as a <mask>.")
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| [{'sequence': '<s>The woman worked as a nurse.</s>',
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| 'score': 0.2667474150657654,
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| 'token': 9008,
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| 'token_str': 'Ġnurse'},
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| {'sequence': '<s>The woman worked as a waitress.</s>',
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| 'score': 0.12280137836933136,
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| 'token': 35698,
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| 'token_str': 'Ġwaitress'},
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| {'sequence': '<s>The woman worked as a teacher.</s>',
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| 'score': 0.09747499972581863,
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| 'token': 3254,
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| 'token_str': 'Ġteacher'},
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| {'sequence': '<s>The woman worked as a secretary.</s>',
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| 'score': 0.05783602222800255,
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| 'token': 2971,
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| 'token_str': 'Ġsecretary'},
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| {'sequence': '<s>The woman worked as a cleaner.</s>',
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| 'score': 0.05576248839497566,
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| 'token': 16126,
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| 'token_str': 'Ġcleaner'}]
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| ```
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|
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| This bias will also affect all fine-tuned versions of this model.
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|
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| ## Training data
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|
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| The RoBERTa model was pretrained on the reunion of five datasets:
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| - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
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| - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
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| - [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
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| articles crawled between September 2016 and February 2019.
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| - [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
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| train GPT-2,
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| - [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
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| story-like style of Winograd schemas.
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|
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| Together theses datasets weight 160GB of text.
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|
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| ## Training procedure
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| ### Preprocessing
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| The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
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| the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
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| with `<s>` and the end of one by `</s>`
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|
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| The details of the masking procedure for each sentence are the following:
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| - 15% of the tokens are masked.
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| - In 80% of the cases, the masked tokens are replaced by `<mask>`.
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|
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| - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
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| - In the 10% remaining cases, the masked tokens are left as is.
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| Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
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|
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| ### Pretraining
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| The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The
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| optimizer used is Adam with a learning rate of 4e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
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| \\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 30,000 steps and linear decay of the learning
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| rate after.
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|
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| ## Evaluation results
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| When fine-tuned on downstream tasks, this model achieves the following results:
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|
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| Glue test results:
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| | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
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| |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
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| | | 90.2 | 92.2 | 94.7 | 96.4 | 68.0 | 96.4 | 90.9 | 86.6 |
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|
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|
|
| ### BibTeX entry and citation info
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|
|
| ```bibtex
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| @article{DBLP:journals/corr/abs-1907-11692,
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| author = {Yinhan Liu and
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| Myle Ott and
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| Naman Goyal and
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| Jingfei Du and
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| Mandar Joshi and
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| Danqi Chen and
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| Omer Levy and
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| Mike Lewis and
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| Luke Zettlemoyer and
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| Veselin Stoyanov},
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| title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
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| journal = {CoRR},
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| volume = {abs/1907.11692},
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| year = {2019},
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| url = {http://arxiv.org/abs/1907.11692},
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| archivePrefix = {arXiv},
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| eprint = {1907.11692},
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| timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
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| biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
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| bibsource = {dblp computer science bibliography, https://dblp.org}
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| }
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| ```
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|
|
| <a href="https://huggingface.co/exbert/?model=roberta-base">
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| <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
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| </a>
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| |
