README.md

metadata

title: Text Summarization
emoji: 📝
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 3.35.2
app_file: app.py
pinned: false

📝 Text Summarization with Transformers

This Hugging Face Space demonstrates how to perform text summarization using a pre-trained model from the transformers library. It provides a simple interface to generate a concise summary from a longer text document.

This project is part of a portfolio showcasing foundational AI/ML skills. It highlights the ability to leverage powerful, pre-trained models for common Natural Language Processing (NLP) tasks.

🚀 How to Use the Demo

1. Enter Text: Paste the long-form text you want to summarize into the "Original Text" input box.
2. Generate Summary: Click the "Summarize" button.
3. View Result: The generated summary will appear in the "Summarized Text" box on the right.

🧠 Model Information

This demo uses the sshleifer/distilbart-cnn-12-6 model, a sequence-to-sequence model fine-tuned on the CNN/DailyMail dataset for summarization. It is a distilled version of the BART model, making it faster and more efficient while retaining high performance.

🛠️ Technical Details

• Framework: The interactive demo is built with Gradio.
• Library: The core summarization logic is powered by the Hugging Face transformers library.
• Hardware: The application is configured to leverage a GPU if one is available, which significantly speeds up the summarization process.
• Workflow: The project follows a standard development workflow, including dependency management (requirements.txt), refactoring for testability, and unit testing (unittest).