transformers/docs/source/en/model_doc/xlm-roberta-xl.md

<!--Copyright 2022 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->

# XLM-RoBERTa-XL

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The XLM-RoBERTa-XL model was proposed in [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau. 

The abstract from the paper is the following:

*Recent work has demonstrated the effectiveness of cross-lingual language model pretraining for cross-lingual understanding. In this study, we present the results of two larger multilingual masked language models, with 3.5B and 10.7B parameters. Our two new models dubbed XLM-R XL and XLM-R XXL outperform XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages. This suggests pretrained models with larger capacity may obtain both strong performance on high-resource languages while greatly improving low-resource languages. We make our code and models publicly available.*

This model was contributed by [Soonhwan-Kwon](https://github.com/Soonhwan-Kwon) and [stefan-it](https://huggingface.co/stefan-it). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).

## Usage tips

XLM-RoBERTa-XL 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.

## Resources

- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)

## XLMRobertaXLConfig

[[autodoc]] XLMRobertaXLConfig

## XLMRobertaXLModel

[[autodoc]] XLMRobertaXLModel
    - forward

## XLMRobertaXLForCausalLM

[[autodoc]] XLMRobertaXLForCausalLM
    - forward

## XLMRobertaXLForMaskedLM

[[autodoc]] XLMRobertaXLForMaskedLM
    - forward

## XLMRobertaXLForSequenceClassification

[[autodoc]] XLMRobertaXLForSequenceClassification
    - forward

## XLMRobertaXLForMultipleChoice

[[autodoc]] XLMRobertaXLForMultipleChoice
    - forward

## XLMRobertaXLForTokenClassification

[[autodoc]] XLMRobertaXLForTokenClassification
    - forward

## XLMRobertaXLForQuestionAnswering

[[autodoc]] XLMRobertaXLForQuestionAnswering
    - forward