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# XLM

## Overview

The XLM model was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by
Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:

- a causal language modeling (CLM) objective (next token prediction),
- a masked language modeling (MLM) objective (BERT-like), or
- a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)

The abstract from the paper is the following:

*Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We
propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our
approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we
obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised
machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the
previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*

Tips:

- XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
  select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the [multi-lingual](../multilingual) page for more information.

This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/facebookresearch/XLM/).


## XLMConfig

[[autodoc]] XLMConfig

## XLMTokenizer

[[autodoc]] XLMTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## XLM specific outputs

[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput

## XLMModel

[[autodoc]] XLMModel
    - forward

## XLMWithLMHeadModel

[[autodoc]] XLMWithLMHeadModel
    - forward

## XLMForSequenceClassification

[[autodoc]] XLMForSequenceClassification
    - forward

## XLMForMultipleChoice

[[autodoc]] XLMForMultipleChoice
    - forward

## XLMForTokenClassification

[[autodoc]] XLMForTokenClassification
    - forward

## XLMForQuestionAnsweringSimple

[[autodoc]] XLMForQuestionAnsweringSimple
    - forward

## XLMForQuestionAnswering

[[autodoc]] XLMForQuestionAnswering
    - forward

## TFXLMModel

[[autodoc]] TFXLMModel
    - call

## TFXLMWithLMHeadModel

[[autodoc]] TFXLMWithLMHeadModel
    - call

## TFXLMForSequenceClassification

[[autodoc]] TFXLMForSequenceClassification
    - call

## TFXLMForMultipleChoice

[[autodoc]] TFXLMForMultipleChoice
    - call

## TFXLMForTokenClassification

[[autodoc]] TFXLMForTokenClassification
    - call

## TFXLMForQuestionAnsweringSimple

[[autodoc]] TFXLMForQuestionAnsweringSimple
    - call
