
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:
thon

leverage checkpoints for Bert2Bert model
use BERT's cls token as BOS token and sep token as EOS token
encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-large-uncased", bos_token_id=101, eos_token_id=102)
add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
decoder = BertGenerationDecoder.from_pretrained(
     "google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
 )
bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
create tokenizer
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
input_ids = tokenizer(
     "This is a long article to summarize", add_special_tokens=False, return_tensors="pt"
 ).input_ids
labels = tokenizer("This is a short summary", return_tensors="pt").input_ids
train
loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
loss.backward()

Pretrained [EncoderDecoderModel] are also directly available in the model hub, e.g.:
thon

instantiate sentence fusion model
sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
input_ids = tokenizer(
     "This is the first sentence. This is the second sentence.", add_special_tokens=False, return_tensors="pt"
 ).input_ids
outputs = sentence_fuser.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Tips:

[BertGenerationEncoder] and [BertGenerationDecoder] should be used in
  combination with [EncoderDecoder].
For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input.
  Therefore, no EOS token should be added to the end of the input.

BertGenerationConfig
[[autodoc]] BertGenerationConfig
BertGenerationTokenizer
[[autodoc]] BertGenerationTokenizer
    - save_vocabulary
BertGenerationEncoder
[[autodoc]] BertGenerationEncoder
    - forward
BertGenerationDecoder
[[autodoc]] BertGenerationDecoder
    - forward