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ReStructuredText
95 lines
3.9 KiB
ReStructuredText
ProphetNet
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**DISCLAIMER:** If you see something strange, file a `Github Issue
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<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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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The ProphetNet model was proposed in `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,
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<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 pre-training 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 pre-training corpus.*
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The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
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ProphetNetConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetConfig
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:members:
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ProphetNetTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetTokenizer
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:members:
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ProphetNet specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
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:members:
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
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:members:
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderModelOutput
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:members:
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderLMOutput
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:members:
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ProphetNetModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetModel
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:members: forward
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ProphetNetEncoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetEncoder
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:members: forward
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ProphetNetDecoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetDecoder
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:members: forward
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ProphetNetForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetForConditionalGeneration
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:members: forward
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ProphetNetForCausalLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetForCausalLM
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:members: forward
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