Task 1: News Topic Classifier Using BERT

Objective

Fine-tune a BERT transformer model to classify news headlines into topic categories using transfer learning and deploy as an interactive web application.

Dataset

Source: AG News Dataset (Hugging Face)
URL: https://huggingface.co/datasets/ag_news

Full Dataset:

• Training: 120,000 samples
• Testing: 7,600 samples

Subset Used (for disk space optimization):

• Training: 10,000 samples
• Testing: 2,000 samples

Categories (4 classes): 0. World - International news, politics, conflicts

1. Sports - Games, athletes, tournaments
2. Business - Markets, companies, economy
3. Sci/Tech - Technology, science, innovations

Sample Headlines:

Class 0 (World): "Iran Urges UN to Reject US Demand on Nuclear Program"
Class 1 (Sports): "Yankees Beat Red Sox 3-2 in Extra Innings"
Class 2 (Business): "Google Stock Surges After Earnings Beat Expectations"
Class 3 (Sci/Tech): "NASA Rover Discovers Evidence of Ancient Water on Mars"

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Technologies Used

Technology	Purpose	Version
Python	Programming	3.8+
PyTorch	Deep learning framework	2.0+
Transformers	BERT model & tokenizer	4.30+
Datasets	Dataset loading	2.12+
scikit-learn	Evaluation metrics	1.0+
matplotlib/seaborn	Visualization	Latest
Streamlit	Web deployment	1.25+

How to Run

Prerequisites

Install Dependencies:

pip install torch torchvision torchaudio
pip install transformers datasets accelerate
pip install matplotlib seaborn scikit-learn

# Load saved model
from deploy_classifier import classify_news

# Test
headline = "Tesla announces new electric vehicle"
result = classify_news(headline)

print(f"Category: {result['category']}")
print(f"Confidence: {result['confidence']:.2%}")

Running Web App

# Install Streamlit
pip install streamlit

# Run app
streamlit run app_streamlit.py