BigBird-Pegasus Large BigPatent
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | View Paper |
| Framework | PyTorch |
| Task | Text Summarization |
What is bigbird-pegasus-large-bigpatent?
BigBird-Pegasus is an advanced transformer-based model specifically designed for handling long-sequence document summarization. Developed by Google, it implements a novel block sparse attention mechanism that enables processing of sequences up to 4096 tokens, significantly longer than traditional transformer models. This particular variant is fine-tuned on the BigPatent dataset for patent document summarization.
Implementation Details
The model employs a sophisticated architecture combining BigBird's sparse attention mechanism with Pegasus's summarization capabilities. It can be configured with different attention types and includes customizable parameters such as block_size and num_random_blocks.
- Block sparse attention with configurable block size (default: 64)
- Adjustable number of random blocks (default: 3)
- Support for both sparse and full attention modes
- Optimized for long document processing
Core Capabilities
- Long document summarization up to 4096 tokens
- Efficient processing through sparse attention mechanism
- Specialized for patent document handling
- Flexible attention configuration options
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to handle extremely long patent documents through its block sparse attention mechanism, making it computationally efficient while maintaining high performance on summarization tasks.
Q: What are the recommended use cases?
The model is specifically optimized for summarizing patent documents and other long technical texts. It's particularly suitable for applications requiring processing of lengthy documents where traditional transformer models might be computationally prohibitive.
