shaadclt / Huggingface-Blog-Post-Summarizer

This project provides a blog post summarizer using Hugging Face's Transformers library. It allows you to generate concise summaries of long blog posts or articles using state-of-the-art natural language processing models.

Blog Post Summarizer using Hugging Face

This project provides a blog post summarizer using Hugging Face's Transformers library. It allows you to generate concise summaries of long blog posts or articles using state-of-the-art natural language processing models.

Table of Contents

• Introduction
• How it Works
• Setup
• Usage
• Contributing
• License

Introduction

The Blog Post Summarizer project utilizes Hugging Face's Transformers library, which is built on top of the PyTorch deep learning framework, to generate abstractive summaries of blog posts. It leverages pre-trained language models such as GPT-2, BERT, or T5 to generate high-quality summaries.

How it Works

The project follows these steps to summarize a blog post:

1. Preprocessing: The input blog post is preprocessed by removing any unwanted characters or HTML tags, and the text is tokenized into smaller units.

2. Model Loading: A pre-trained language model from the Hugging Face model hub is loaded. You can choose models such as GPT-2, BERT, T5, etc., depending on your summarization needs.

3. Encoding: The preprocessed text is encoded into numerical representations that the model can understand.

4. Summarization: The encoded text is passed through the loaded model to generate a summary. The model's output is typically longer than the desired summary length.

5. Post-processing: The generated summary is post-processed to remove any unwanted tokens and ensure coherence and readability.

6. Output: The final summary is returned as the output of the summarizer.

Setup

To use this project locally, follow these steps:

1. Clone the repository:

https://github.com/shaadclt/Huggingface-Blog-Post-Summarizer.git

2. Install the required dependencies by running:

pip install -r requirements.txt

Usage

1. Run the app.py script:

streamlit run app.py

2. Paste the desired Blog Post URL.

3. Receive the generated summary as the output and utilize it for further analysis or display.

Feel free to customize the summarizer according to your specific requirements, such as adjusting the summary length or experimenting with different pre-trained models.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvement, please open an issue or submit a pull request on the project's GitHub repository.

License

This project is licensed under the MIT License. You are free to modify and use the code for both personal and commercial purposes.