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-large-README.md

README.md

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+---
+language: en
+tags:
+- exbert
+license: mit
+datasets:
+- bookcorpus
+- wikipedia
+---
+
+# RoBERTa large 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 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-large')
+>>> unmasker("Hello I'm a <mask> model.")
+
+[{'sequence': "<s>Hello I'm a male model.</s>",
+  'score': 0.3317350447177887,
+  'token': 2943,
+  'token_str': 'Ġmale'},
+ {'sequence': "<s>Hello I'm a fashion model.</s>",
+  'score': 0.14171843230724335,
+  'token': 2734,
+  'token_str': 'Ġfashion'},
+ {'sequence': "<s>Hello I'm a professional model.</s>",
+  'score': 0.04291723668575287,
+  'token': 2038,
+  'token_str': 'Ġprofessional'},
+ {'sequence': "<s>Hello I'm a freelance model.</s>",
+  'score': 0.02134818211197853,
+  'token': 18150,
+  'token_str': 'Ġfreelance'},
+ {'sequence': "<s>Hello I'm a young model.</s>",
+  'score': 0.021098261699080467,
+  'token': 664,
+  'token_str': 'Ġyoung'}]
+```
+
+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-large')
+model = RobertaModel.from_pretrained('roberta-large')
+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-large')
+model = TFRobertaModel.from_pretrained('roberta-large')
+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-large')
+>>> unmasker("The man worked as a <mask>.")
+
+[{'sequence': '<s>The man worked as a mechanic.</s>',
+  'score': 0.08260300755500793,
+  'token': 25682,
+  'token_str': 'Ġmechanic'},
+ {'sequence': '<s>The man worked as a driver.</s>',
+  'score': 0.05736079439520836,
+  'token': 1393,
+  'token_str': 'Ġdriver'},
+ {'sequence': '<s>The man worked as a teacher.</s>',
+  'score': 0.04709019884467125,
+  'token': 3254,
+  'token_str': 'Ġteacher'},
+ {'sequence': '<s>The man worked as a bartender.</s>',
+  'score': 0.04641604796051979,
+  'token': 33080,
+  'token_str': 'Ġbartender'},
+ {'sequence': '<s>The man worked as a waiter.</s>',
+  'score': 0.04239227622747421,
+  'token': 38233,
+  'token_str': 'Ġwaiter'}]
+
+>>> unmasker("The woman worked as a <mask>.")
+
+[{'sequence': '<s>The woman worked as a nurse.</s>',
+  'score': 0.2667474150657654,
+  'token': 9008,
+  'token_str': 'Ġnurse'},
+ {'sequence': '<s>The woman worked as a waitress.</s>',
+  'score': 0.12280137836933136,
+  'token': 35698,
+  'token_str': 'Ġwaitress'},
+ {'sequence': '<s>The woman worked as a teacher.</s>',
+  'score': 0.09747499972581863,
+  'token': 3254,
+  'token_str': 'Ġteacher'},
+ {'sequence': '<s>The woman worked as a secretary.</s>',
+  'score': 0.05783602222800255,
+  'token': 2971,
+  'token_str': 'Ġsecretary'},
+ {'sequence': '<s>The woman worked as a cleaner.</s>',
+  'score': 0.05576248839497566,
+  'token': 16126,
+  'token_str': 'Ġcleaner'}]
+```
+
+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 theses datasets weight 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 token 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 4e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
+\\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 30,000 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  |
+|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
+|      | 90.2 | 92.2 | 94.7 | 96.4  | 68.0 | 96.4  | 90.9 | 86.6 |
+
+
+### 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>