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GPT-3 – YouTube script generator with Python for beginners
GPT-3 is a large language model created by OpenAI. It is trained on 175 billion parameters and is trying to emulate human natural language. Now that you know a bit about GPT-3, let’s take a look at how you can use Python to get started with this powerful tool.
Python is a great language to use with GPT-3 because it is easy to use and has a wide range of libraries and tools. In this blog post, we will take a look at how you can use Python with GPT-3 to write a summary of a text.
We will also look at how you can use a few shot approach to get even more specific output from GPT-3.
Read more, or watch the YouTube video:
YouTube: Introduction to using Python with GPT-3I guess we all really enjoy using OpenAI`s playground in GPT-3, and it really is a great space to learn and explore the large language models.
But if you want to build something with GPT-3, you kinda need to use the API in some way in my opinion. So here I think Python is a great way to get started.
Python is not that hard to learn the basics of and you could find 1000`s of free tutorials online.
When using GPT-3 with Python you can do much more than in the playground. Some examples are:
Running loops
Combine GPT-3 with other API`s
Fine Tuning of your own GPT-3 model
Much more ++
Explaining a simple GPT-3 Python scriptI really recommend watching the YouTube video for this section
Let’s look at an example of this:
Write a summary of the following text with headings and paragraphs:
WRITE A SUMMARY:
This could be scraped content from the web, or just some copy paste content.
As I said this is one of the easiest ways to use Python with GPT-3, but it is a great way to learn the basics.
No shot or Few shot in GPT-3 What is the difference between no shot training and few shot training in machine learning?No shot training is when a machine learning algorithm is trained on data without any prior knowledge or examples to learn from. This can be done through unsupervised learning methods.
Few shot training is when a machine learning algorithm is given a few examples to learn from before being trained on data. This can be done through either supervised or unsupervised learning methods.
This will usually work pretty good. But if you want a very specific output, you should consider giving GPT-3 some examples of what you are looking for. (few shot)
This could be an example of this:
Write a summary of the following text with headings and paragraphs:
Example 1:
WRITE A SUMMARY:
GPT-3 is a large language model created by OpenAI. It is trained on 175 billion parameters and is trying to emulate human natural language.
Example 2:
WRITE A SUMMARY:
Here we have an example of what kind of output we want, and that is what we call a few shot approach in machine learning.
So if you are struggling getting the output you want from GPT-3. I would definitely recommend trying out the few shot approach with GPT-3. The instruct series has of course reduced the need for few shot examples, but sometimes it can work very well.
GPT-3 + Python Conclusion SummaryIn conclusion, python is a great language for working with GPT-3 because it is easy to use and has a wide range of libraries and tools.
In this blog post, we have looked at how you can use Python with GPT-3 to write a summary of a text. We have also seen how you can use a few shot approach to get even more specific output from GPT-3.
GPT-3 is a powerful tool that can be used to generate text summaries. Python is a great language to use with GPT-3 because it is easy to use and has a wide range of libraries and tools.
GPT-3 can be used for a wide range of tasks, such as automatic text summarization, text generation, and language translation.
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