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* Reorganize doc for multilingual support * Fix style * Style * Toc trees * Adapt templates
125 lines
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125 lines
3.9 KiB
Plaintext
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# XLM
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## Overview
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The XLM model was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by
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Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:
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- a causal language modeling (CLM) objective (next token prediction),
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- a masked language modeling (MLM) objective (BERT-like), or
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- a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)
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The abstract from the paper is the following:
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*Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
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In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We
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propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
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data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
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state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our
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approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we
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obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised
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machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the
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previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*
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Tips:
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- XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
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select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
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- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the [multi-lingual](../multilingual) page for more information.
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This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/facebookresearch/XLM/).
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## XLMConfig
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[[autodoc]] XLMConfig
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## XLMTokenizer
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[[autodoc]] XLMTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## XLM specific outputs
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[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
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## XLMModel
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[[autodoc]] XLMModel
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- forward
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## XLMWithLMHeadModel
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[[autodoc]] XLMWithLMHeadModel
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- forward
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## XLMForSequenceClassification
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[[autodoc]] XLMForSequenceClassification
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- forward
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## XLMForMultipleChoice
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[[autodoc]] XLMForMultipleChoice
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- forward
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## XLMForTokenClassification
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[[autodoc]] XLMForTokenClassification
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- forward
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## XLMForQuestionAnsweringSimple
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[[autodoc]] XLMForQuestionAnsweringSimple
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- forward
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## XLMForQuestionAnswering
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[[autodoc]] XLMForQuestionAnswering
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- forward
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## TFXLMModel
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[[autodoc]] TFXLMModel
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- call
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## TFXLMWithLMHeadModel
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[[autodoc]] TFXLMWithLMHeadModel
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- call
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## TFXLMForSequenceClassification
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[[autodoc]] TFXLMForSequenceClassification
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- call
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## TFXLMForMultipleChoice
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[[autodoc]] TFXLMForMultipleChoice
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- call
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## TFXLMForTokenClassification
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[[autodoc]] TFXLMForTokenClassification
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- call
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## TFXLMForQuestionAnsweringSimple
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[[autodoc]] TFXLMForQuestionAnsweringSimple
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- call
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