This project utilizes the power of the Hugging Face Transformers library to provide a simple and efficient way to summarize text using a pre-trained language model. The tool allows you to extract key information and generate concise summaries from lengthy text.
In today's information-rich world, it's often challenging to read and comprehend long documents. Text summarization can be a valuable tool for quickly understanding the main points of an article, report, or any piece of text. This project offers a user-friendly web application that leverages the capabilities of large language models to provide text summarization.
- Utilizes the Hugging Face Transformers library.
- Offers a web-based user interface for text input.
- Generates concise summaries based on the provided text.
- Customizable summary length parameters.
- Simple and intuitive user experience.
To get started with this project, follow these steps:
-
Clone this repository to your local machine:
git clone https://github.com/Rafe2001/Text-Summarization-LLM.git
-
Install the necessary dependencies:
pip install -r requirements.txt
-
Run the application:
streamlit run app.py
-
Visit the application in your web browser at
http://localhost:8501to start summarizing text.
-
Open the web application in your browser.
-
Enter the text you want to summarize in the provided text area.
-
Click the "Summarize" button.
-
The application will process the input text and display a summarized version of the text.
-
You can adjust the summarization parameters (min length, max length) in the code as needed.
Contributions are welcome! If you'd like to contribute to this project, please follow these guidelines:
- Fork the project and create a new branch.
- Make your changes and ensure that the code works.
- Submit a pull request with a clear description of your changes.
This project is licensed under the MIT License - see the LICENSE file for details.