metadata

license: cc-by-sa-3.0
task_categories:
  - text-classification
  - text-generation
  - summarization
language:
  - en
tags:
  - medium
  - medium articles
  - nlp
  - corpus
  - web-scraped
  - text
  - blog-posts
  - large-scale
  - english
pretty_name: Medium Articles Corpus (10K Sample)
size_categories:
  - 10K<n<100K

Medium Articles Corpus (10K Sample)

The Medium Articles Corpus is a massive, clean dataset of articles scraped from Medium.com. This sample version contains 10,000 articles + and is designed to showcase the quality and structure of the full corpus for researchers and developers. This is the subset from the large dataset https://crawlfeeds.com/websites/medium/articles

Dataset Features

This dataset includes the following key features, provided in a single, well-structured CSV file:

title: The headline of the article.
content: The full body text of the article.
tags: The topics and keywords associated with the article.
author: The name of the article's author.
date: The publication date of the article.
claps: The number of claps (upvotes) the article received.

Use Cases

This dataset is ideal for:

Language Model Prototyping: A manageable corpus for quickly testing and validating language model architectures.
Topic Modeling: Discovering popular and emerging trends on the platform using unsupervised learning methods.
Text Classification: Training small-scale models to automatically categorize articles by topic, genre, or sentiment.
Exploratory Data Analysis: Getting a feel for the dataset's structure, quality, and content.

Dataset Structure

The data is provided in a single CSV file with the following columns:

Column Name	Description	Example
`title`	The article headline	"How I Built a Recommendation Engine"
`content`	The full article text	"In this article, I will..."
`tags`	Comma-separated list of tags	"AI, Machine Learning, Python"
`author`	The author's name	"Author Name"
`date`	Publication date	"2023-10-27"
`claps`	Number of claps	1500

How to Use

You can easily load this dataset using the Hugging Face datasets library.