| | --- |
| | 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 a 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 <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](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books; |
| | - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ; |
| | - [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news |
| | articles crawled between September 2016 and February 2019. |
| | - [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to |
| | train GPT-2, |
| | - [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the |
| | story-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 of |
| | the model take pieces of 512 contiguous tokens that may span over documents. The beginning of a new document is marked |
| | with `<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. The |
| | optimizer used is Adam with a learning rate of 6e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and |
| | \\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learning |
| | rate after. |
| |
|
| | ## Evaluation results |
| |
|
| | When fine-tuned on downstream tasks, this model achieves the following results: |
| |
|
| | Glue test results: |
| |
|
| | | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |
| | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| |
| | | | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 | |
| |
|
| |
|
| | ### BibTeX entry and citation info |
| |
|
| | ```bibtex |
| | @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} |
| | } |
| | ``` |
| |
|
| | <a href="https://huggingface.co/exbert/?model=roberta-base"> |
| | <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
| | </a> |
| | |
