
XLM-RoBERTa

Overview
The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
data.
The abstract from the paper is the following:
This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a
wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
will make XLM-R code, data, and models publicly available.
This model was contributed by stefan-it. The original code can be found here.
Usage tips

XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
  not require lang tensors to understand which language is used, and should be able to determine the correct
  language from the input ids.
Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective. It only uses masked language modeling on sentences coming from one language.

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with XLM-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.

A blog post on how to finetune XLM RoBERTa for multiclass classification with Habana Gaudi on AWS
[XLMRobertaForSequenceClassification] is supported by this example script and notebook.
[TFXLMRobertaForSequenceClassification] is supported by this example script and notebook.
[FlaxXLMRobertaForSequenceClassification] is supported by this example script and notebook.
Text classification chapter of the 🤗 Hugging Face Task Guides.
Text classification task guide

[XLMRobertaForTokenClassification] is supported by this example script and notebook.
[TFXLMRobertaForTokenClassification] is supported by this example script and notebook.
[FlaxXLMRobertaForTokenClassification] is supported by this example script.
Token classification chapter of the 🤗 Hugging Face Course.
Token classification task guide

[XLMRobertaForCausalLM] is supported by this example script and notebook.
Causal language modeling chapter of the 🤗 Hugging Face Task Guides.
Causal language modeling task guide

[XLMRobertaForMaskedLM] is supported by this example script and notebook.
[TFXLMRobertaForMaskedLM] is supported by this example script and notebook.
[FlaxXLMRobertaForMaskedLM] is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling

[XLMRobertaForQuestionAnswering] is supported by this example script and notebook.
[TFXLMRobertaForQuestionAnswering] is supported by this example script and notebook.
[FlaxXLMRobertaForQuestionAnswering] is supported by this example script.
Question answering chapter of the 🤗 Hugging Face Course.
Question answering task guide

Multiple choice

[XLMRobertaForMultipleChoice] is supported by this example script and notebook.
[TFXLMRobertaForMultipleChoice] is supported by this example script and notebook.
Multiple choice task guide

🚀 Deploy

A blog post on how to Deploy Serverless XLM RoBERTa on AWS Lambda.

 
This implementation is the same as RoBERTa. Refer to the documentation of RoBERTa for usage examples as well as the information relative to the inputs and outputs.

XLMRobertaConfig
[[autodoc]] XLMRobertaConfig
XLMRobertaTokenizer
[[autodoc]] XLMRobertaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
XLMRobertaTokenizerFast
[[autodoc]] XLMRobertaTokenizerFast

XLMRobertaModel
[[autodoc]] XLMRobertaModel
    - forward
XLMRobertaForCausalLM
[[autodoc]] XLMRobertaForCausalLM
    - forward
XLMRobertaForMaskedLM
[[autodoc]] XLMRobertaForMaskedLM
    - forward
XLMRobertaForSequenceClassification
[[autodoc]] XLMRobertaForSequenceClassification
    - forward
XLMRobertaForMultipleChoice
[[autodoc]] XLMRobertaForMultipleChoice
    - forward
XLMRobertaForTokenClassification
[[autodoc]] XLMRobertaForTokenClassification
    - forward
XLMRobertaForQuestionAnswering
[[autodoc]] XLMRobertaForQuestionAnswering
    - forward

TFXLMRobertaModel
[[autodoc]] TFXLMRobertaModel
    - call
TFXLMRobertaForCausalLM
[[autodoc]] TFXLMRobertaForCausalLM
    - call
TFXLMRobertaForMaskedLM
[[autodoc]] TFXLMRobertaForMaskedLM
    - call
TFXLMRobertaForSequenceClassification
[[autodoc]] TFXLMRobertaForSequenceClassification
    - call
TFXLMRobertaForMultipleChoice
[[autodoc]] TFXLMRobertaForMultipleChoice
    - call
TFXLMRobertaForTokenClassification
[[autodoc]] TFXLMRobertaForTokenClassification
    - call
TFXLMRobertaForQuestionAnswering
[[autodoc]] TFXLMRobertaForQuestionAnswering
    - call

FlaxXLMRobertaModel
[[autodoc]] FlaxXLMRobertaModel
    - call
FlaxXLMRobertaForCausalLM
[[autodoc]] FlaxXLMRobertaForCausalLM
    - call
FlaxXLMRobertaForMaskedLM
[[autodoc]] FlaxXLMRobertaForMaskedLM
    - call
FlaxXLMRobertaForSequenceClassification
[[autodoc]] FlaxXLMRobertaForSequenceClassification
    - call
FlaxXLMRobertaForMultipleChoice
[[autodoc]] FlaxXLMRobertaForMultipleChoice
    - call
FlaxXLMRobertaForTokenClassification
[[autodoc]] FlaxXLMRobertaForTokenClassification
    - call
FlaxXLMRobertaForQuestionAnswering
[[autodoc]] FlaxXLMRobertaForQuestionAnswering
    - call

