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69 lines
2.9 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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# XLM-ProphetNet
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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
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@patrickvonplaten
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## Overview
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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
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Zhang, Ming Zhou on 13 Jan, 2020.
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XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of
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just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual
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"wiki100" Wikipedia dump.
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The abstract from the paper is the following:
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*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
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self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
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the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
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n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
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step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
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overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
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dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
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abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
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state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
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The Authors' code can be found [here](https://github.com/microsoft/ProphetNet).
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## XLMProphetNetConfig
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[[autodoc]] XLMProphetNetConfig
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## XLMProphetNetTokenizer
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[[autodoc]] XLMProphetNetTokenizer
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## XLMProphetNetModel
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[[autodoc]] XLMProphetNetModel
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## XLMProphetNetEncoder
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[[autodoc]] XLMProphetNetEncoder
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## XLMProphetNetDecoder
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[[autodoc]] XLMProphetNetDecoder
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## XLMProphetNetForConditionalGeneration
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[[autodoc]] XLMProphetNetForConditionalGeneration
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## XLMProphetNetForCausalLM
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[[autodoc]] XLMProphetNetForCausalLM
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