Migrate model card from transformers-repo
Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755 Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/roberta-base-README.md
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Julien Chaumond
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---
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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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# RoBERTa base model
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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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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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## Model description
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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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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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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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## Intended uses & limitations
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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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### How to use
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You can use this model directly with a pipeline for masked language modeling:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='roberta-base')
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>>> unmasker("Hello I'm a <mask> model.")
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[{'sequence': "<s>Hello I'm a male model.</s>",
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'score': 0.3306540250778198,
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'token': 2943,
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'token_str': 'Ġmale'},
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{'sequence': "<s>Hello I'm a female model.</s>",
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'score': 0.04655390977859497,
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'token': 2182,
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'token_str': 'Ġfemale'},
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{'sequence': "<s>Hello I'm a professional model.</s>",
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'score': 0.04232972860336304,
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'token': 2038,
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'token_str': 'Ġprofessional'},
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{'sequence': "<s>Hello I'm a fashion model.</s>",
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'score': 0.037216778844594955,
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'token': 2734,
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'token_str': 'Ġfashion'},
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{'sequence': "<s>Hello I'm a Russian model.</s>",
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'score': 0.03253649175167084,
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'token': 1083,
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'token_str': 'ĠRussian'}]
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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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```python
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from transformers import RobertaTokenizer, RobertaModel
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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = RobertaModel.from_pretrained('roberta-base')
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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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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-base')
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model = TFRobertaModel.from_pretrained('roberta-base')
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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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### 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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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='roberta-base')
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>>> unmasker("The man worked as a <mask>.")
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[{'sequence': '<s>The man worked as a mechanic.</s>',
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'score': 0.08702439814805984,
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'token': 25682,
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'token_str': 'Ġmechanic'},
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{'sequence': '<s>The man worked as a waiter.</s>',
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'score': 0.0819653645157814,
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'token': 38233,
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'token_str': 'Ġwaiter'},
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{'sequence': '<s>The man worked as a butcher.</s>',
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'score': 0.073323555290699,
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'token': 32364,
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'token_str': 'Ġbutcher'},
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{'sequence': '<s>The man worked as a miner.</s>',
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'score': 0.046322137117385864,
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'token': 18678,
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'token_str': 'Ġminer'},
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{'sequence': '<s>The man worked as a guard.</s>',
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'score': 0.040150221437215805,
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'token': 2510,
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'token_str': 'Ġguard'}]
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>>> unmasker("The Black woman worked as a <mask>.")
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[{'sequence': '<s>The Black woman worked as a waitress.</s>',
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'score': 0.22177888453006744,
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'token': 35698,
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'token_str': 'Ġwaitress'},
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{'sequence': '<s>The Black woman worked as a prostitute.</s>',
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'score': 0.19288744032382965,
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'token': 36289,
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'token_str': 'Ġprostitute'},
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{'sequence': '<s>The Black woman worked as a maid.</s>',
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'score': 0.06498628109693527,
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'token': 29754,
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'token_str': 'Ġmaid'},
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{'sequence': '<s>The Black woman worked as a secretary.</s>',
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'score': 0.05375480651855469,
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'token': 2971,
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'token_str': 'Ġsecretary'},
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{'sequence': '<s>The Black woman worked as a nurse.</s>',
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'score': 0.05245552211999893,
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'token': 9008,
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'token_str': 'Ġnurse'}]
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```
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This bias will also affect all fine-tuned versions of this model.
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## Training data
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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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Together theses datasets weight 160GB of text.
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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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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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- 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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### 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 6e-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 24,000 steps and linear decay of the learning
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rate after.
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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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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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| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 |
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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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