README.md · MongoDB/tech-news-embeddings at main

metadata

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 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"
    }
  ]
}