---
license: apache-2.0
task_categories:
- text-generation
- text-classification
- question-answering
- summarization
language:
- en
tags:
- Articles
- Content
- Machine-Learning
- Deep-Learning
size_categories:
- 10K<n<100K
---

# Content-Articles Dataset

## Overview
The Content-Articles dataset is a collection of academic articles and research papers across various subjects, including Computer Science, Physics, and Mathematics. This dataset is designed to facilitate research and analysis in these fields by providing structured data on article titles, abstracts, and subject classifications.

## Dataset Details

### Modalities
- **Tabular**: The dataset is structured in a tabular format.
- **Text**: Contains text data, including article titles and abstracts.

### Formats
- **CSV**: The dataset is available in CSV format.

### Size
- The dataset contains between 10,000 to 100,000 entries.

### Libraries
- **Datasets**: The dataset is compatible with the Hugging Face Datasets library.
- **Pandas**: Can be easily loaded and manipulated using Pandas.
- **Croissant**: Additional support for Croissant format.

## Dataset Structure

### Columns
- **TITLE**: The title of the article.
- **ABSTRACT**: The abstract of the article.
- **Computer Science**: Binary indicator (1 or 0) for articles related to Computer Science.
- **Physics**: Binary indicator (1 or 0) for articles related to Physics.
- **Mathematics**: Binary indicator (1 or 0) for articles related to Mathematics.

### Splits
- **train**: Contains 21,000 rows of training data.

## Usage

### Loading the Dataset
You can load the dataset using the Hugging Face Datasets library:

```python
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Content-Articles")
```

### Example
Here’s an example of how to access the data:

```python
import pandas as pd

# Load the dataset
dataset = load_dataset("prithivMLmods/Content-Articles")

# Convert to Pandas DataFrame
df = pd.DataFrame(dataset['train'])

# Display the first few rows
print(df.head())
```