Summarization and Classification Project

This project uses a pretrained transformer model to summarize and classify text data efficiently. It can be used for natural language processing tasks such as text summarization and topic classification.

Features

• Summarizes input text into a concise version
• Classifies text into predefined categories
• Uses Hugging Face transformers and pipelines

Installation

You can run this project in Google Colab or locally with Python installed. Required packages:

pip install transformers torch

## Usage
Here is a simple example of how to use the summarization and classification functions:

from transformers import pipeline

# Load summarization pipeline
summarizer = pipeline("summarization")

# Load text classification pipeline
classifier = pipeline("text-classification")

text = "Soft robotics integrates compliant materials and innovative design principles to create flexible and adaptive robotic systems."

# Summarize
summary = summarizer(text, max_length=50, min_length=10, do_sample=False)
print("Summary:", summary[0]['summary_text'])

# Classify
classification = classifier(text)
print("Classification:", classification)


Demo Output
Original Text:
Soft robotics integrates compliant materials and innovative design principles to create flexible and adaptive robotic systems.

Summarized:
Soft robotics uses soft materials and new design methods for adaptable robots.

Classification Result:
[{'label': 'TECHNOLOGY', 'score': 0.99}]

License
This project is licensed under the MIT License.