longformer-base-4096
Maintainer: allenai
146
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The longformer-base-4096 is a transformer model developed by the Allen Institute for Artificial Intelligence (AI2), a non-profit institute focused on high-impact AI research and engineering. It is a BERT-like model that has been pre-trained on long documents using masked language modeling. The key innovation of this model is its use of a combination of sliding window (local) attention and global attention, which allows it to handle sequences of up to 4,096 tokens.
The longformer-base-4096 model is similar to other long-context transformer models like LongLLaMA and BTLM-3B-8k-base, which have also been designed to handle longer input sequences than standard transformer models.
Model inputs and outputs
Inputs
- Text sequence: The
longformer-base-4096model can process text sequences of up to 4,096 tokens.
Outputs
- Masked language modeling logits: The primary output of the model is a set of logits representing the probability distribution over the vocabulary for each masked token in the input sequence.
Capabilities
The longformer-base-4096 model is designed to excel at tasks that involve processing long documents, such as summarization, question answering, and document classification. Its ability to handle longer input sequences makes it particularly useful for applications where the context is spread across multiple paragraphs or pages.
What can I use it for?
The longformer-base-4096 model can be fine-tuned on a variety of downstream tasks, such as text summarization, question answering, and document classification. It could be particularly useful for applications that involve processing long-form content, such as research papers, legal documents, or technical manuals.
Things to try
One interesting aspect of the longformer-base-4096 model is its use of global attention, which allows the model to learn task-specific representations. Experimenting with different configurations of global attention could be a fruitful area of exploration, as it may help the model perform better on specific tasks.
Additionally, the model's ability to handle longer input sequences could be leveraged for tasks that require a more holistic understanding of a document, such as long-form question answering or document-level sentiment analysis.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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