metadata

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:

from datasets import load_dataset

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

Example

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

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())