flan-t5-base-samsum

Maintainer: philschmid

Total Score

81

Last updated 5/23/2024

⚙️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The flan-t5-base-samsum model is a fine-tuned version of the google/flan-t5-base model on the samsum dataset. It achieves strong performance on text summarization tasks, with results including a loss of 1.3716, Rouge1 of 47.2358, Rouge2 of 23.5135, Rougel of 39.6266, and Rougelsum of 43.3458.

Model inputs and outputs

The flan-t5-base-samsum model takes in text data and generates summarized output text. It can be used for a variety of text-to-text tasks beyond just summarization.

Inputs

  • Text data to be summarized or transformed

Outputs

  • Summarized or transformed text data

Capabilities

The flan-t5-base-samsum model demonstrates strong capabilities in text summarization, able to concisely capture the key points of longer input text. It could be used for tasks like summarizing news articles, meeting notes, or other lengthy documents.

What can I use it for?

The flan-t5-base-samsum model could be useful for automating text summarization in a variety of business and research applications. For example, it could help teams quickly process and synthesize large amounts of information, or provide summaries for customer support agents to reference. The model's flexibility also means it could potentially be fine-tuned for other text-to-text tasks beyond just summarization.

Things to try

One interesting thing to try with the flan-t5-base-samsum model is using it for interactive summarization, where the user can provide feedback and the model can iteratively refine the summary. This could help ensure the most salient points are captured. Another idea is to use the model in a pipeline with other NLP components, such as topic modeling or sentiment analysis, to gain deeper insights from text data.



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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