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---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- vector search
- semantic search
- retrieval augmented generation
pretty_name: hackernoon_tech_news_with_embeddings
size_categories:
- 100K<n<1M
---
## Overview
[HackerNoon](https://huggingface.co/datasets/HackerNoon/tech-company-news-data-dump/tree/main) curated the internet's most cited 7M+ tech company news articles and blog posts about the 3k+ most valuable tech companies in 2022 and 2023.
To further enhance the dataset's utility, a new embedding field and vector embedding for every datapoint have been added using the OpenAI EMBEDDING_MODEL = "text-embedding-3-small", with an EMBEDDING_DIMENSION of 256.
Notably, this extension with vector embeddings only contains a portion of the original dataset, focusing on enriching a selected subset with advanced analytical capabilities.
## Dataset Structure
Each record in the dataset represents a news article about technology companies and includes the following fields:
- _id: A unique identifier for the news article.
- companyName: The name of the company the news article is about.
- companyUrl: A URL to the HackerNoon company profile page for the company.
- published_at: The date and time when the news article was published.
- url: A URL to the original news article.
- title: The title of the news article.
- main_image: A URL to the main image of the news article.
- description: A brief summary of the news article's content.
- embedding: An array of numerical values representing the vector embedding for the article, generated using the OpenAI EMBEDDING_MODEL.
## Usage
The dataset is suited for a range of applications, including:
- Tracking and analyzing trends in the tech industry.
- Enhancing search and recommendation systems for tech news content with the use of vector embeddings.
- Conducting sentiment analysis and other natural language processing tasks to gauge public perception and impact of news on specific tech companies.
- Educational purposes in data science, journalism, and technology studies courses.
## Notes
### Sample Document
```
{
"_id": {
"$oid": "65c63ea1f187c085a866f680"
},
"companyName": "01Synergy",
"companyUrl": "https://hackernoon.com/company/01synergy",
"published_at": "2023-05-16 02:09:00",
"url": "https://www.businesswire.com/news/home/20230515005855/en/onsemi-and-Sineng-Electric-Spearhead-the-Development-of-Sustainable-Energy-Applications/",
"title": "onsemi and Sineng Electric Spearhead the Development of Sustainable Energy Applications",
"main_image": "https://firebasestorage.googleapis.com/v0/b/hackernoon-app.appspot.com/o/images%2Fimageedit_25_7084755369.gif?alt=media&token=ca7527b0-a214-46d4-af72-1062b3df1458",
"description": "(Nasdaq: ON) a leader in intelligent power and sensing technologies today announced that Sineng Electric will integrate onsemi EliteSiC silic",
"embedding": [
{
"$numberDouble": "0.05243798345327377"
},
{
"$numberDouble": "-0.10347484797239304"
},
{
"$numberDouble": "-0.018149614334106445"
}
]
}
``` |