readme: ---
  language: en
  license: apache-2.0
  datasets:
  - scientific_papers
  tags:
  - summarization
  model-index:
  - name: google/bigbird-pegasus-large-pubmed
    results:
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: scientific_papers
        type: scientific_papers
        config: pubmed
        split: test
      metrics:
      - name: ROUGE-1
        type: rouge
        value: 40.8966
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 18.1161
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 26.1743
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 34.2773
        verified: true
      - name: loss
        type: loss
        value: 2.1707184314727783
        verified: true
      - name: meteor
        type: meteor
        value: 0.3513
        verified: true
      - name: gen_len
        type: gen_len
        value: 221.2531
        verified: true
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: scientific_papers
        type: scientific_papers
        config: arxiv
        split: test
      metrics:
      - name: ROUGE-1
        type: rouge
        value: 40.3815
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 14.374
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 23.4773
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 33.772
        verified: true
      - name: loss
        type: loss
        value: 3.235051393508911
        verified: true
      - name: gen_len
        type: gen_len
        value: 186.2003
        verified: true
  ---
  
  # 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](https://arxiv.org/abs/2007.14062) and first released in this [repository](https://github.com/google-research/bigbird).
  
  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:
  
  ```python
  from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-pubmed")
  
  # by default encoder-attention is `block_sparse` with num_random_blocks=3, block_size=64
  model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed")
  
  # 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-pubmed", attention_type="original_full")
  
  # you can change `block_size` & `num_random_blocks` like this:
  model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed", 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 **pubmed dataset** from [scientific_papers](https://huggingface.co/datasets/scientific_papers).
  
  ## BibTeX entry and citation info
  
  ```tex
  @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}
  }
  ```
  