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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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# MUPPET RoBERTa large model
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This is a Massive Multi-task Pre-finetuned version of Roberta large. It was introduced in
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[this paper](https://arxiv.org/abs/2101.11038). The model improves over roberta-base in a wide range of GLUE, QA tasks (details can be found in the paper). The gains in
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smaller datasets are significant.
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Note: This checkpoint does not contain the classificaiton/MRC heads used during pre-finetuning due to compatibility issues and hence you might get slightly lower performance than that reported in the paper on some datasets
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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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## 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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| Model | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | SQuAD|
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|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:----:|
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| Roberta-large | 90.2 | 92.2 | 94.7 | 96.4 | 63.6 | 91.2 | 90.9 | 88.1 | 88.7|
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| MUPPET Roberta-large | 90.8 | 92.2 | 94.9 | 97.4 | - | - | 91.4 | 92.8 | 89.4|
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### BibTeX entry and citation info
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```bibtex
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@article{DBLP:journals/corr/abs-2101-11038,
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author = {Armen Aghajanyan and
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Anchit Gupta and
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Akshat Shrivastava and
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Xilun Chen and
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Luke Zettlemoyer and
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Sonal Gupta},
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title = {Muppet: Massive Multi-task Representations with Pre-Finetuning},
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journal = {CoRR},
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volume = {abs/2101.11038},
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year = {2021},
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url = {https://arxiv.org/abs/2101.11038},
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archivePrefix = {arXiv},
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eprint = {2101.11038},
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timestamp = {Sun, 31 Jan 2021 17:23:50 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-2101-11038.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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``` |
