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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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