# XLM-ProphetNet

**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://arxiv.org/abs/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