Longformer-base-4096
| Property | Value |
|---|---|
| Author | Allen AI |
| License | Apache 2.0 |
| Paper | Longformer: The Long-Document Transformer |
| Downloads | 5.6M+ |
What is longformer-base-4096?
Longformer-base-4096 is an innovative transformer model specifically designed to handle long documents with sequences up to 4,096 tokens. Built upon the RoBERTa architecture, it introduces a unique attention mechanism that combines sliding window attention with global attention, making it particularly efficient for processing lengthy texts while maintaining computational feasibility.
Implementation Details
The model implements a hybrid attention mechanism that significantly reduces the quadratic complexity typical of traditional transformers. It was pretrained using Masked Language Modeling (MLM) on long documents and incorporates user-configurable global attention patterns that can be customized for specific tasks.
- Maximum sequence length: 4,096 tokens
- Based on RoBERTa architecture
- Combines local and global attention mechanisms
- Supports both PyTorch and TensorFlow implementations
Core Capabilities
- Processing long documents efficiently
- Customizable attention patterns
- Masked Language Modeling
- Support for various downstream NLP tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to process documents up to 4,096 tokens while maintaining efficiency through its innovative attention mechanism that combines both local and global attention patterns.
Q: What are the recommended use cases?
This model is particularly well-suited for tasks involving long documents such as document classification, question answering on long texts, and document summarization where context from the entire document is important.
