Update README.md

README.md

@@ -8,15 +8,14 @@ datasets:
 - wikipedia
 ---
 
-# RoBERTa large model
+# MUPPET 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.
+This is a Massive Multi-task Pre-finetuned version of Roberta large. It was introduced in
+[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
+smaller datasets are significant. 
+
+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
 
-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
 
@@ -44,154 +43,6 @@ Note that this model is primarily aimed at being fine-tuned on tasks that use th
 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:
@@ -200,36 +51,33 @@ 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 |
+|      | 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
+@article{DBLP:journals/corr/abs-2101-11038,
+  author    = {Armen Aghajanyan and
+               Anchit Gupta and
+               Akshat Shrivastava and
+               Xilun Chen and
                Luke Zettlemoyer and
-               Veselin Stoyanov},
-  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
+               Sonal Gupta},
+  title     = {Muppet: Massive Multi-task Representations with Pre-Finetuning},
   journal   = {CoRR},
-  volume    = {abs/1907.11692},
-  year      = {2019},
-  url       = {http://arxiv.org/abs/1907.11692},
+  volume    = {abs/2101.11038},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2101.11038},
   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},
+  eprint    = {2101.11038},
+  timestamp = {Sun, 31 Jan 2021 17:23:50 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2101-11038.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 href="https://huggingface.co/facebook/muppet-roberta-large">
+\\\\t<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
 </a>