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*This model was released on 2020-01-13 and added to Hugging Face Transformers on 2023-06-20.*

# XLM-ProphetNet

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This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.

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**DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) and assign
@patrickvonplaten

## Overview

The XLM-ProphetNet model was proposed in [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,](https://huggingface.co/papers/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
Zhang, Ming Zhou on 13 Jan, 2020.

XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of
just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual
"wiki100" Wikipedia dump. XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained on the cross-lingual dataset XGLUE.

The abstract from the paper is the following:

*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*

The Authors' code can be found [here](https://github.com/microsoft/ProphetNet).

## Resources

- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)

## XLMProphetNetConfig

[[autodoc]] XLMProphetNetConfig

## XLMProphetNetTokenizer

[[autodoc]] XLMProphetNetTokenizer

## XLMProphetNetModel

[[autodoc]] XLMProphetNetModel

## XLMProphetNetEncoder

[[autodoc]] XLMProphetNetEncoder

## XLMProphetNetDecoder

[[autodoc]] XLMProphetNetDecoder

## XLMProphetNetForConditionalGeneration

[[autodoc]] XLMProphetNetForConditionalGeneration

## XLMProphetNetForCausalLM

[[autodoc]] XLMProphetNetForCausalLM
