Led Base Book Summary

🚀 BigBird for Long Sequence Summarization

This project focuses on using the BigBird model to handle long - sequence NLP tasks, especially text summarization, with reduced computational cost compared to traditional Transformer - based models.

🚀 Quick Start

The BigBird model is designed to address the high computational cost issue of traditional Transformer - based models when dealing with long sequences. It uses block sparse attention, which enables it to handle sequences up to a length of 4096 at a much lower computational cost.

✨ Features

• Long Sequence Handling: Can handle sequences up to 4096 in length, suitable for long - document summarization and other long - context tasks.
• Computational Efficiency: Reduces the time and memory complexity compared to standard Transformer - based models like BERT.
• SOTA Performance: Achieves state - of - the - art results on various tasks involving very long sequences.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The following is an example of considering a sentence to understand how attention works in the context of BigBird:

# let's consider following sentence as an example
example = ['BigBird', 'is', 'now', 'available', 'in', 'HuggingFace', 'for', 'extractive', 'question', 'answering']

# further let's assume, we're trying to understand the representation of 'available' i.e.
query_token = 'available'
# We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section.
key_tokens = []
# => currently 'available' token doesn't have anything to attend

Advanced Usage

The model can be used for long - document summarization. For example, when dealing with long texts from books, plays, etc., it can generate summaries at different granularity levels (paragraph, chapter, and book - level).

📚 Documentation

• Model Limitations: Transformer - based models have an $O(n^2)$ time and memory complexity, where $n$ is the sequence length. BigBird addresses this by using block sparse attention.
• Comparison with Other Models: Several recent papers, such as Longformer, Performer, Reformer, and Clustered attention, also try to approximate the full attention matrix to solve the long - sequence problem. BigBird is one of the models that can handle long sequences more efficiently.
• Attention Mechanism: In BERT - like attention, every word attends to all other tokens. In BigBird, we can think about a more sensible choice of key tokens for a queried token to attend to, considering the concept of sliding attention.

🔧 Technical Details

• Block Sparse Attention: BigBird relies on block sparse attention instead of normal attention (like BERT's attention). This allows it to handle long sequences with lower computational cost.
• Computational Complexity: It can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT.

📄 License

This project is licensed under the following licenses:

• Apache - 2.0
• BSD - 3 - Clause