The abstract from the paper is the following:

*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
propose novel applications to genomics data.*

This model was contributed by [vasudevgupta](https://huggingface.co/vasudevgupta). The original code can be found
[here](https://github.com/google-research/bigbird).

## Usage tips
The context provides a clear explanation of the main advantage of the BigBird model over traditional Transformers-based models like BERT. It highlights that BigBird uses a sparse attention mechanism, which reduces the quadratic dependency on sequence length to linear, allowing it to handle much longer sequences. This is a significant improvement over the full attention mechanism used in models like BERT, which has a quadratic dependency on sequence length. The context also mentions that this capability allows BigBird to improve performance on various NLP tasks, which further supports the main advantage. Therefore, the question is clearly and unambiguously answerable with the given context.


The question is highly relevant to machine learning developers working with NLP applications, especially those using the Hugging Face ecosystem. Understanding the advantages of the BigBird model over traditional Transformers like BERT is crucial for developers who need to handle long sequences efficiently. BigBird's ability to process longer sequences with reduced computational complexity can significantly impact the performance and scalability of NLP models. This knowledge can guide developers in selecting the appropriate model for their specific use cases, making the question very useful.


The question is asking about the main advantage of the BigBird model compared to traditional Transformers-based models like BERT. It is clear and specific, and it does not rely on any additional context to be understood. The question is self-contained and can be answered by someone familiar with the topic or with access to relevant documentation.

