Bigbird Pegasus Large Bigpatent

🚀 BigBirdPegasus model (large)

BigBirdPegasus is a powerful model that extends Transformer-based models to handle longer sequences. It uses block sparse attention, enabling efficient processing of long texts and achieving state-of-the-art results in long document summarization and other tasks.

🚀 Quick Start

BigBird, a sparse-attention based transformer, extends Transformer-based models like BERT to handle much longer sequences. It comes with a theoretical understanding of what a full transformer can do, which the sparse model can manage. 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.

✨ Features

BigBird relies on block sparse attention instead of normal attention (i.e., BERT's attention). It 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 document summarization and question-answering with long contexts.

💻 Usage Examples

Basic Usage

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)

📚 Documentation

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

📄 License

This project is licensed under the Apache-2.0 license.

📚 Citation

@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}
}