long-t5-tglobal-base-16384-book-summary
Maintainer: pszemraj
117
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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 long-t5-tglobal-base-16384-book-summary is a fine-tuned version of the google/long-t5-tglobal-base model on the kmfoda/booksum dataset. This model is designed to summarize long text, providing a concise and coherent summary of the content. It generalizes well to academic and narrative text, and can generate "SparkNotes-esque" summaries on a variety of topics.
Model inputs and outputs
Inputs
- Long text: The model can handle long input sequences up to 16,384 tokens.
Outputs
- Summary text: The model generates a summary of the input text, with a maximum output length of 1,024 tokens.
Capabilities
The long-t5-tglobal-base-16384-book-summary model excels at summarizing long-form text. It can digest large amounts of information and distill the key points into a concise summary. This makes it useful for tasks like academic paper summarization, novel chapter summaries, or condensing lengthy articles.
What can I use it for?
The long-t5-tglobal-base-16384-book-summary model can be leveraged in a variety of applications that require summarizing long-form text. For example, you could use it to automatically generate summaries of research papers or book chapters, saving time and effort for readers. It could also be integrated into content curation platforms to provide users with high-level overviews of lengthy articles or reports.
Things to try
One interesting use case for this model is to generate summaries of niche or obscure topics. The model's ability to generalize across domains means it can likely provide useful summaries even for relatively specialized content. You could experiment with feeding the model lengthy passages on topics like ancient history, modern philosophy, or cutting-edge scientific research, and see the concise summaries it produces.
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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