mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 14:20:04 +06:00
08f534d2da
* Important files
* Styling them all
* Revert "Styling them all"
This reverts commit 7d029395fd.
* Syling them for realsies
* Fix syntax error
* Fix benchmark_utils
* More fixes
* Fix modeling auto and script
* Remove new line
* Fixes
* More fixes
* Fix more files
* Style
* Add FSMT
* More fixes
* More fixes
* More fixes
* More fixes
* Fixes
* More fixes
* More fixes
* Last fixes
* Make sphinx happy
76 lines
3.5 KiB
ReStructuredText
76 lines
3.5 KiB
ReStructuredText
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.
|
|
|
|
The abstract from the paper is the following:
|
|
|
|
*In this paper, we present a new sequence-to-sequence pre-training 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 pre-training corpus.*
|
|
|
|
The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
|
|
|
|
XLMProphetNetConfig
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMProphetNetConfig
|
|
:members:
|
|
|
|
|
|
XLMProphetNetTokenizer
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMProphetNetTokenizer
|
|
:members:
|
|
|
|
|
|
XLMProphetNetModel
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMProphetNetModel
|
|
|
|
|
|
XLMProphetNetEncoder
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMProphetNetEncoder
|
|
|
|
|
|
XLMProphetNetDecoder
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMProphetNetDecoder
|
|
|
|
|
|
XLMProphetNetForConditionalGeneration
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMProphetNetForConditionalGeneration
|
|
|
|
|
|
XLMProphetNetForCausalLM
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMProphetNetForCausalLM
|