Project 2: News Categorization Using BERT
4. Step 2: Loading and Preparing the Dataset
For this project, we'll utilize a comprehensive news categorization dataset that's publicly available. The AG News dataset is an excellent choice for this task, as it provides a well-structured and balanced collection of news articles. This dataset consists of approximately 120,000 training samples and 7,600 test samples, making it substantial enough for meaningful model training and evaluation.
The AG News dataset is particularly valuable because it offers:
- Four distinct categories (World, Sports, Business, and Sci/Tech) that cover the most common news domains
- High-quality labeled data that has been professionally curated
- A balanced distribution of articles across categories
- Articles of varying lengths and complexity, providing a realistic training scenario
Each article in the dataset includes both the headline and description text, allowing the model to learn from both concise summaries and detailed content. This structure makes it ideal for training a robust news categorization system that can handle real-world applications.
Load the Dataset
from datasets import load_dataset
# Load the AG News dataset
dataset = load_dataset('ag_news')
# Check the dataset structure
print(dataset)The dataset will have a train and test split, with each entry containing the text of the news article and its corresponding label (category).
4. Step 2: Loading and Preparing the Dataset
For this project, we'll utilize a comprehensive news categorization dataset that's publicly available. The AG News dataset is an excellent choice for this task, as it provides a well-structured and balanced collection of news articles. This dataset consists of approximately 120,000 training samples and 7,600 test samples, making it substantial enough for meaningful model training and evaluation.
The AG News dataset is particularly valuable because it offers:
- Four distinct categories (World, Sports, Business, and Sci/Tech) that cover the most common news domains
- High-quality labeled data that has been professionally curated
- A balanced distribution of articles across categories
- Articles of varying lengths and complexity, providing a realistic training scenario
Each article in the dataset includes both the headline and description text, allowing the model to learn from both concise summaries and detailed content. This structure makes it ideal for training a robust news categorization system that can handle real-world applications.
Load the Dataset
from datasets import load_dataset
# Load the AG News dataset
dataset = load_dataset('ag_news')
# Check the dataset structure
print(dataset)The dataset will have a train and test split, with each entry containing the text of the news article and its corresponding label (category).
4. Step 2: Loading and Preparing the Dataset
For this project, we'll utilize a comprehensive news categorization dataset that's publicly available. The AG News dataset is an excellent choice for this task, as it provides a well-structured and balanced collection of news articles. This dataset consists of approximately 120,000 training samples and 7,600 test samples, making it substantial enough for meaningful model training and evaluation.
The AG News dataset is particularly valuable because it offers:
- Four distinct categories (World, Sports, Business, and Sci/Tech) that cover the most common news domains
- High-quality labeled data that has been professionally curated
- A balanced distribution of articles across categories
- Articles of varying lengths and complexity, providing a realistic training scenario
Each article in the dataset includes both the headline and description text, allowing the model to learn from both concise summaries and detailed content. This structure makes it ideal for training a robust news categorization system that can handle real-world applications.
Load the Dataset
from datasets import load_dataset
# Load the AG News dataset
dataset = load_dataset('ag_news')
# Check the dataset structure
print(dataset)The dataset will have a train and test split, with each entry containing the text of the news article and its corresponding label (category).
4. Step 2: Loading and Preparing the Dataset
For this project, we'll utilize a comprehensive news categorization dataset that's publicly available. The AG News dataset is an excellent choice for this task, as it provides a well-structured and balanced collection of news articles. This dataset consists of approximately 120,000 training samples and 7,600 test samples, making it substantial enough for meaningful model training and evaluation.
The AG News dataset is particularly valuable because it offers:
- Four distinct categories (World, Sports, Business, and Sci/Tech) that cover the most common news domains
- High-quality labeled data that has been professionally curated
- A balanced distribution of articles across categories
- Articles of varying lengths and complexity, providing a realistic training scenario
Each article in the dataset includes both the headline and description text, allowing the model to learn from both concise summaries and detailed content. This structure makes it ideal for training a robust news categorization system that can handle real-world applications.
Load the Dataset
from datasets import load_dataset
# Load the AG News dataset
dataset = load_dataset('ag_news')
# Check the dataset structure
print(dataset)The dataset will have a train and test split, with each entry containing the text of the news article and its corresponding label (category).