README.md

metadata

language: en
license: apache-2.0
tags:
  - summarization
datasets:
  - big_patent
model-index:
  - name: google/bigbird-pegasus-large-bigpatent
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: test
        metrics:
          - type: rouge
            value: 8.6864
            name: ROUGE-1
            verified: true
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            value: 0.7795
            name: ROUGE-2
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          - type: rouge
            value: 7.1464
            name: ROUGE-L
            verified: true
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          - type: rouge
            value: 7.0344
            name: ROUGE-LSUM
            verified: true
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          - type: loss
            value: 6.242544651031494
            name: loss
            verified: true
            verifyToken: >-
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            value: 96.8579
            name: gen_len
            verified: true
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BigBirdPegasus model (large)

BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.

BigBird was introduced in this paper and first released in this repository.

Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

BigBird relies on block sparse attention instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts.

How to use

Here is how to use this model to get the features of a given text in PyTorch:

from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-bigpatent")

# by default encoder-attention is `block_sparse` with num_random_blocks=3, block_size=64
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent")

# decoder attention type can't be changed & will be "original_full"
# you can change `attention_type` (encoder only) to full attention like this:
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent", attention_type="original_full")

# you can change `block_size` & `num_random_blocks` like this:
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent", block_size=16, num_random_blocks=2)

text = "Replace me by any text you'd like."
inputs = tokenizer(text, return_tensors='pt')
prediction = model.generate(**inputs)
prediction = tokenizer.batch_decode(prediction)

Training Procedure

This checkpoint is obtained after fine-tuning BigBirdPegasusForConditionalGeneration for summarization on big_patent dataset.

BibTeX entry and citation info

@misc{zaheer2021big,
      title={Big Bird: Transformers for Longer Sequences}, 
      author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed},
      year={2021},
      eprint={2007.14062},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}