104 lines
4.0 KiB
Markdown
104 lines
4.0 KiB
Markdown
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# ProphetNet
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<div class="flex flex-wrap space-x-1">
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<a href="https://huggingface.co/models?filter=prophetnet">
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<img alt="Models" src="https://img.shields.io/badge/All_model_pages-prophetnet-blueviolet">
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</a>
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<a href="https://huggingface.co/spaces/docs-demos/prophetnet-large-uncased">
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<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
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</a>
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</div>
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## Overview
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The 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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ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just
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the next token.
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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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## Usage tips
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- ProphetNet is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
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the left.
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- The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.
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## Resources
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- [Causal language modeling task guide](../tasks/language_modeling)
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- [Translation task guide](../tasks/translation)
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- [Summarization task guide](../tasks/summarization)
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## ProphetNetConfig
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[[autodoc]] ProphetNetConfig
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## ProphetNetTokenizer
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[[autodoc]] ProphetNetTokenizer
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## ProphetNet specific outputs
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[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
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[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
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[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput
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[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput
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## ProphetNetModel
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[[autodoc]] ProphetNetModel
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- forward
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## ProphetNetEncoder
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[[autodoc]] ProphetNetEncoder
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- forward
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## ProphetNetDecoder
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[[autodoc]] ProphetNetDecoder
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- forward
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## ProphetNetForConditionalGeneration
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[[autodoc]] ProphetNetForConditionalGeneration
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- forward
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## ProphetNetForCausalLM
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[[autodoc]] ProphetNetForCausalLM
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- forward
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