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Exploring GPT-3

Exploring GPT-3

By : Tingiris
4.3 (16)
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Exploring GPT-3

Exploring GPT-3

4.3 (16)
By: Tingiris

Overview of this book

Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text that is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API. If you want to get hands-on with leveraging artificial intelligence for natural language processing (NLP) tasks, this easy-to-follow book will help you get started. Beginning with a high-level introduction to NLP and GPT-3, the book takes you through practical examples that show how to leverage the OpenAI API and GPT-3 for text generation, classification, and semantic search. You'll explore the capabilities of the OpenAI API and GPT-3 and find out which NLP use cases GPT-3 is best suited for. You’ll also learn how to use the API and optimize requests for the best possible results. With examples focusing on the OpenAI Playground and easy-to-follow JavaScript and Python code samples, the book illustrates the possible applications of GPT-3 in production. By the end of this book, you'll understand the best use cases for GPT-3 and how to integrate the OpenAI API in your applications for a wide array of NLP tasks.
Table of Contents (15 chapters)
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1
Section 1: Understanding GPT-3 and the OpenAI API
4
Section 2: Getting Started with GPT-3
8
Section 3: Using the OpenAI API

Understanding content filtering

Content filtering is about blocking or hiding content that may be deemed offensive, inappropriate, or even dangerous. In our case, we're talking about content that GPT-3 generates that we don't want users of our application to see.

To filter potentially offensive or unsafe text, we'll need to write a little bit of code to evaluate text that GPT-3 generates and classify it as safe, sensitive, or unsafe. The cool part is that we can use GPT-3 to do the classifications. So, it's kind of like self-policing but with a bit of help from our code.

At a high level, here is how we make it work:

  1. GPT-3 generates a completion to a prompt.
  2. The completion text is submitted back to a GPT-3 filter engine.
  3. The filter engine returns a classification (safe, sensitive, unsafe).
  4. The original completion text is blocked or sent back to the user based on the classification.
  5. Optionally, if the completion text is sensitive, or...
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