pegasus_summarizer
Maintainer: tuner007
43
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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 pegasus_summarizer model is a fine-tuned version of the PEGASUS model for the task of text summarization. It was created by tuner007, and is available on the Hugging Face model repository. Similar models include the pegasus_paraphrase model, which is fine-tuned for paraphrasing, and the financial-summarization-pegasus model, which is fine-tuned for summarizing financial news articles.
Model inputs and outputs
The pegasus_summarizer model takes in a text input and generates a summarized version of that text as output. The input text can be up to 1024 tokens long, and the model will generate a summary that is up to 128 tokens long.
Inputs
- Input text: The text that the model will summarize.
Outputs
- Summary text: The summarized version of the input text, generated by the model.
Capabilities
The pegasus_summarizer model is capable of generating concise and accurate summaries of input text. It can be used to summarize a wide variety of text, including news articles, academic papers, and blog posts. The model has been trained on a large corpus of text data, which allows it to generate summaries that capture the key points and main ideas of the input.
What can I use it for?
The pegasus_summarizer model can be used for a variety of applications, such as:
- Content summarization: Automatically generating summaries of long-form content to help users quickly understand the key points.
- Workflow automation: Integrating the model into a workflow to summarize incoming text data, such as customer support inquiries or internal documentation.
- Research and analysis: Summarizing research papers or other academic literature to help researchers quickly identify relevant information.
Things to try
One interesting thing to try with the pegasus_summarizer model is to experiment with the generation parameters, such as the num_beams and temperature values. Adjusting these parameters can change the length and style of the generated summaries, allowing you to fine-tune the model's output to your specific needs.
Another interesting thing to try is to compare the summaries generated by the pegasus_summarizer model to those generated by other summarization models, such as the financial-summarization-pegasus model. This can help you understand the strengths and weaknesses of each model and choose the one that best fits your use case.
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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