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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.
## Data Ingestion
[Create a free MongoDB Atlas Account](https://www.mongodb.com/cloud/atlas/register?utm_campaign=devrel&utm_source=community&utm_medium=organic_social&utm_content=Hugging%20Face%20Dataset&utm_term=richmond.alake) and conduct the Data Ingestion process below.
```python
import os
from pymongo import MongoClient
import datasets
from datasets import load_dataset
from bson import json_util
# MongoDB Atlas URI and client setup
uri = os.environ.get('MONGODB_ATLAS_URI')
client = MongoClient(uri)
# Change to the appropriate database and collection names for the tech news embeddings
db_name = 'your_database_name' # Change this to your actual database name
collection_name = 'tech_news_embeddings' # Change this to your actual collection name
tech_news_embeddings_collection = client[db_name][collection_name]
# Load the "tech-news-embeddings" dataset from Hugging Face
dataset = load_dataset("AIatMongoDB/tech-news-embeddings")
insert_data = []
# Iterate through the dataset and prepare the documents for insertion
for item in dataset['train']:
# Convert the dataset item to MongoDB document format
doc_item = json_util.loads(json_util.dumps(item))
insert_data.append(doc_item)
# Insert in batches of 1000 documents
if len(insert_data) == 1000:
tech_news_embeddings_collection.insert_many(insert_data)
print("1000 records ingested")
insert_data = []
# Insert any remaining documents
if len(insert_data) > 0:
tech_news_embeddings_collection.insert_many(insert_data)
print("Data Ingested")
```
## 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"
}
]
}
``` |