
BertGeneration
Overview
The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
[EncoderDecoderModel] as proposed in Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
The abstract from the paper is the following:
Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By
warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language
Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT,
GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both
encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation,
Text Summarization, Sentence Splitting, and Sentence Fusion.
This model was contributed by patrickvonplaten. The original code can be
found here.
Usage examples and tips
The model can be used in combination with the [EncoderDecoderModel] to leverage two pretrained BERT checkpoints for 
subsequent fine-tuning:
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