From 841d321fa51acc648d605a33c87ebe6726da8153 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Fri, 11 Dec 2020 22:25:32 +0100 Subject: [PATCH] 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-base-README.md --- README.md | 234 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 234 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..3a89cda --- /dev/null +++ b/README.md @@ -0,0 +1,234 @@ +--- +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 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 model.") + +[{'sequence': "Hello I'm a male model.", + 'score': 0.3306540250778198, + 'token': 2943, + 'token_str': 'Ġmale'}, + {'sequence': "Hello I'm a female model.", + 'score': 0.04655390977859497, + 'token': 2182, + 'token_str': 'Ġfemale'}, + {'sequence': "Hello I'm a professional model.", + 'score': 0.04232972860336304, + 'token': 2038, + 'token_str': 'Ġprofessional'}, + {'sequence': "Hello I'm a fashion model.", + 'score': 0.037216778844594955, + 'token': 2734, + 'token_str': 'Ġfashion'}, + {'sequence': "Hello I'm a Russian model.", + '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 .") + +[{'sequence': 'The man worked as a mechanic.', + 'score': 0.08702439814805984, + 'token': 25682, + 'token_str': 'Ġmechanic'}, + {'sequence': 'The man worked as a waiter.', + 'score': 0.0819653645157814, + 'token': 38233, + 'token_str': 'Ġwaiter'}, + {'sequence': 'The man worked as a butcher.', + 'score': 0.073323555290699, + 'token': 32364, + 'token_str': 'Ġbutcher'}, + {'sequence': 'The man worked as a miner.', + 'score': 0.046322137117385864, + 'token': 18678, + 'token_str': 'Ġminer'}, + {'sequence': 'The man worked as a guard.', + 'score': 0.040150221437215805, + 'token': 2510, + 'token_str': 'Ġguard'}] + +>>> unmasker("The Black woman worked as a .") + +[{'sequence': 'The Black woman worked as a waitress.', + 'score': 0.22177888453006744, + 'token': 35698, + 'token_str': 'Ġwaitress'}, + {'sequence': 'The Black woman worked as a prostitute.', + 'score': 0.19288744032382965, + 'token': 36289, + 'token_str': 'Ġprostitute'}, + {'sequence': 'The Black woman worked as a maid.', + 'score': 0.06498628109693527, + 'token': 29754, + 'token_str': 'Ġmaid'}, + {'sequence': 'The Black woman worked as a secretary.', + 'score': 0.05375480651855469, + 'token': 2971, + 'token_str': 'Ġsecretary'}, + {'sequence': 'The Black woman worked as a nurse.', + '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 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 `` and the end of one by `` + +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 ``. +- 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} +} +``` + + + +