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-3
I 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.
Python is advantageous with GPT-3 because it is a high-level interpreted language that is easy to use. Python also has a wide range of libraries and tools that can be used with GPT-3.
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 script
I really recommend watching the YouTube video for this section
In my Youtube video I explain one of the easiest Python scripts you can use with GPT-3. The script uses a text file as the GPT-3 prompt and has a parameter inside the prompt that I call <<FEED>>.
<<FEED>> will then get replaced with the string content from a new text file.
Let’s look at an example of this:
Write a summary of the following text with headings and paragraphs:
<<FEED>>
WRITE A SUMMARY:
The GPT-3 script will now write a summary of any text that gets replaced by <<FEED>>.
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.
In the last paragraph we use a no shot approach to generate a summary of a text that will replace our placeholder <<FEED>>.
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:
<<FEED>>
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:
<<FEED>>
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 Summary
In 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.
