RichmondMongo
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README.md
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[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.
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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.
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Notably, this extension with vector embeddings only contains a portion of the original dataset, focusing on enriching a selected subset with advanced analytical capabilities.
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## Overview
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[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.
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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.
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Notably, this extension with vector embeddings only contains a portion of the original dataset, focusing on enriching a selected subset with advanced analytical capabilities.
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## Dataset Structure
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Each record in the dataset represents a news article about technology companies and includes the following fields:
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- _id: A unique identifier for the news article.
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- companyName: The name of the company the news article is about.
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- companyUrl: A URL to the HackerNoon company profile page for the company.
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- published_at: The date and time when the news article was published.
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- url: A URL to the original news article.
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- title: The title of the news article.
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- main_image: A URL to the main image of the news article.
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- description: A brief summary of the news article's content.
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- embedding: An array of numerical values representing the vector embedding for the article, generated using the OpenAI EMBEDDING_MODEL.
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## Usage
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The dataset is suited for a range of applications, including:
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- Tracking and analyzing trends in the tech industry.
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- Enhancing search and recommendation systems for tech news content with the use of vector embeddings.
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- Conducting sentiment analysis and other natural language processing tasks to gauge public perception and impact of news on specific tech companies.
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- Educational purposes in data science, journalism, and technology studies courses.
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## Notes
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### Sample Document
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{"_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"}]}
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