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.

Usage:

  • The model can be used in combination with the EncoderDecoderModel to leverage two pretrained BERT checkpoints for subsequent fine-tuning.

# leverage checkpoints for Bert2Bert model...
# use BERT's cls token as BOS token and sep token as EOS token
encoder = BertGenerationEncoder.from_pretrained("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("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("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.,

# 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.

The original code can be found here.

BertGenerationConfig¶

BertGenerationTokenizer¶

BertGenerationEncoder¶

BertGenerationDecoder¶