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RoBERTa

Overview

The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018.

It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much larger mini-batches and learning rates.

The abstract from the paper is the following:

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.

This model was contributed by julien-c. The original code can be found here.

Usage tips

  • This implementation is the same as [BertModel] with a tiny embeddings tweak as well as a setup for Roberta pretrained models.

  • RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a different pretraining scheme.

  • RoBERTa doesn't have token_type_ids, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or </s>)

  • Same as BERT with better pretraining tricks:

    • dynamic masking: tokens are masked differently at each epoch, whereas BERT does it once and for all
    • together to reach 512 tokens (so the sentences are in an order than may span several documents)
    • train with larger batches
    • use BPE with bytes as a subunit and not characters (because of unicode characters)

  • CamemBERT is a wrapper around RoBERTa. Refer to this page for usage examples.

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with RoBERTa. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

Multiple choice

RobertaConfig

autodoc RobertaConfig

RobertaTokenizer

autodoc RobertaTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary

RobertaTokenizerFast

autodoc RobertaTokenizerFast - build_inputs_with_special_tokens

RobertaModel

autodoc RobertaModel - forward

RobertaForCausalLM

autodoc RobertaForCausalLM - forward

RobertaForMaskedLM

autodoc RobertaForMaskedLM - forward

RobertaForSequenceClassification

autodoc RobertaForSequenceClassification - forward

RobertaForMultipleChoice

autodoc RobertaForMultipleChoice - forward

RobertaForTokenClassification

autodoc RobertaForTokenClassification - forward

RobertaForQuestionAnswering

autodoc RobertaForQuestionAnswering - forward

TFRobertaModel

autodoc TFRobertaModel - call

TFRobertaForCausalLM

autodoc TFRobertaForCausalLM - call

TFRobertaForMaskedLM

autodoc TFRobertaForMaskedLM - call

TFRobertaForSequenceClassification

autodoc TFRobertaForSequenceClassification - call

TFRobertaForMultipleChoice

autodoc TFRobertaForMultipleChoice - call

TFRobertaForTokenClassification

autodoc TFRobertaForTokenClassification - call

TFRobertaForQuestionAnswering

autodoc TFRobertaForQuestionAnswering - call

FlaxRobertaModel

autodoc FlaxRobertaModel - call

FlaxRobertaForCausalLM

autodoc FlaxRobertaForCausalLM - call

FlaxRobertaForMaskedLM

autodoc FlaxRobertaForMaskedLM - call

FlaxRobertaForSequenceClassification

autodoc FlaxRobertaForSequenceClassification - call

FlaxRobertaForMultipleChoice

autodoc FlaxRobertaForMultipleChoice - call

FlaxRobertaForTokenClassification

autodoc FlaxRobertaForTokenClassification - call

FlaxRobertaForQuestionAnswering

autodoc FlaxRobertaForQuestionAnswering - call