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*This model was released on 2020-01-13 and added to Hugging Face Transformers on 2020-11-16.*

# ProphetNet

<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">
</div>

## Overview

The 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.

ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just
the next token.

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).

## Usage tips

- ProphetNet is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
- The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.

## Resources

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

## ProphetNetConfig

[[autodoc]] ProphetNetConfig

## ProphetNetTokenizer

[[autodoc]] ProphetNetTokenizer

## ProphetNet specific outputs

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput

## ProphetNetModel

[[autodoc]] ProphetNetModel
    - forward

## ProphetNetEncoder

[[autodoc]] ProphetNetEncoder
    - forward

## ProphetNetDecoder

[[autodoc]] ProphetNetDecoder
    - forward

## ProphetNetForConditionalGeneration

[[autodoc]] ProphetNetForConditionalGeneration
    - forward

## ProphetNetForCausalLM

[[autodoc]] ProphetNetForCausalLM
    - forward
