Categorizing text using vector based embeddings

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AI Summary

• Overview of an embeddings categorizer:
  • Matches text to one of 20 categories using embeddings (t5x embeddings 3 large).
  • Returns top three category matches for input text.
• Examples of categorization:
  • “Hawaii” → Travel, History, Education.
  • ”I’m studying to become a mathematician” → Education, Finance, Science.
  • ”flowers” → Fact, Art, Travel.
  • ”sport” → Sport, Travel, Politics.
  • ”Mark Twain” → Travel, Literature, History.
  • ”Hemingway” → Literature, Environment, History.
  • ”Roman Empire” → History, Real Estate, Politics.
  • ”brain” → Psychology, Science, Art.
  • ”Picasso” → Art, Food, Psychology.
  • ”meteor” → Science, Travel, Art.
• Functionality and usage:
  • Can categorize text, chunks of information, or route user intentions.
  • Utilizes a CategoryMatcherApp class and GPTCalls class for API calls to OpenAI.
  • Initializes with 20 categories and pre-computes embeddings for each.
  • User input generates embeddings and finds closest categories based on similarity.
  • Can return a specified number of top matches (default is three).
  • Demonstrates a proof of concept for potential automation in future projects.
• Additional offerings:
  • Code available on Patreon.
  • Over 200 projects accessible for supporters.
  • Mention of other tools and resources:
    • Chat applications at cod.app with 900+ free GPT-powered chat apps.
    • AutoStreamer app for building live websites and content creation.
    • Live stream tutorials on deploying websites.
• Pricing and support:
  • Download all chat applications for $100 on Patreon.
  • AutoStreamer app available for $200 on Patreon.
  • Acknowledgment of viewer support and anticipation for the next video.