MongoDB/tech-news-embeddings

Overview

HackerNoon 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, 1576528 data points, 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 (Partioned)

Create a free MongoDB Atlas Account

import os
import requests
import pandas as pd
from io import BytesIO
from pymongo import MongoClient

# 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]

hf_token = os.environ.get('HF_TOKEN')
headers = {
    "Authorization": f"Bearer {hf_token}"
}

# Downloads 228012 data points
parquet_files = [
    "https://huggingface.co/api/datasets/AIatMongoDB/tech-news-embeddings/parquet/default/train/0000.parquet",
    "https://huggingface.co/api/datasets/AIatMongoDB/tech-news-embeddings/parquet/default/train/0001.parquet",
    "https://huggingface.co/api/datasets/AIatMongoDB/tech-news-embeddings/parquet/default/train/0002.parquet",
    "https://huggingface.co/api/datasets/AIatMongoDB/tech-news-embeddings/parquet/default/train/0003.parquet",
    "https://huggingface.co/api/datasets/AIatMongoDB/tech-news-embeddings/parquet/default/train/0004.parquet",
    "https://huggingface.co/api/datasets/AIatMongoDB/tech-news-embeddings/parquet/default/train/0005.parquet",
]

all_dataframes = []
combined_df = None

for parquet_file_url in parquet_files:
    response = requests.get(parquet_file_url, headers=headers)
    if response.status_code == 200:

        parquet_bytes = BytesIO(response.content)
        df = pd.read_parquet(parquet_bytes)
        all_dataframes.append(df)
    else:
        print(f"Failed to download Parquet file from {parquet_file_url}: {response.status_code}")

if all_dataframes:
    combined_df = pd.concat(all_dataframes, ignore_index=True)
else:
    print("No dataframes to concatenate.")


# Ingest to database
dataset_records = combined_df.to_dict('records')
tech_news_embeddings_collection.insert_many(dataset_records)

Data Ingestion (All Records)

Create a free MongoDB Atlas Account

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
# The script below ingests 1000 records into the database at a time
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"
    }
  ]
}