GPT-3 For Text Classification [Our 6 Favorite Examples With Code]

As I’m sure you know, GPT-3 is an incredible tool that has taken the world by storm for its capability to generate human-like text.

GPT-3 offers a unique mix of accuracy, speed, and scalability that sets it apart from other natural language models.

But GPT-3’s ability to generate text may not even be GPT-3’s best talent – as it can do an impressive job of understanding text.

It can quickly process natural language and accurately interpret the context in which it is used.

This blog post will explore just how good GPT-3 is at understanding language.

We’ll provide 6 examples below and let you judge for yourself.

With its ability to classify text into various categories, GPT-3 can be used to help people solve complex problems faster and more efficiently than ever before.

So join us as we dig into the depths of GPT-3’s capabilities and explore how it can help us better understand written communication and analyze it in several ways!

Table of Contents show

Language Detection #1

Language detection is important in natural language processing (NLP) systems.

Knowing what type of language is being given is essential for algorithms to process and classify the text effectively.

This helps machines understand and organize vast amounts of data in different languages worldwide.

GPT-3, a state-of-the-art NLP system, can easily detect and classify languages with high accuracy.

It uses sophisticated algorithms to accurately determine the specific properties of any given text – such as word distribution and grammatical structures – to distinguish one language from another.

With its powerful capabilities, GPT-3 has become an invaluable tool for several industries that heavily rely on understanding large amounts of multilingual data.

import openai

openai.api_key = 'key_here'

p = f'''Tell me what language this is 배 안 고파요'''

# generate the response
response = openai.Completion.create(
      engine="davinci-instruct-beta-v3",
      prompt=p,
      temperature=.7,
      max_tokens=500,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0,
      stop=["12."]
    )

# grab our text from the repsonse
text = response['choices'][0]['text']


print(text+'\n')

Language Transformation #2

Language transformation is one of the most important aspects of a great NLP model, which enables the system to identify what language is being used and then use it to transform it into another language.

GPT-3 is a prime example of an NLP system that can be used for this purpose, with its ability to be an all-in-one classifier.

I mean, what else would we expect from OpenAI at this point?

Their APIs seem to be leading the way regarding transformers and machine learning.

As such, language transformation can provide immense benefits to businesses and organizations that rely on automated tools for understanding and decoding languages they haven’t seen before while still being able to get the contextual meaning.

Additionally, it can also help people better understand how natural language works by allowing machines to convert spoken language into digital formats that can be easily understood by other computers.

Language Transformation is not an easy field to dabble in; GPT -3 makes it seem like an easy task.

p = f'''Transform this sentence from korean to english: 배 안 고파요'''

# generate the response
response = openai.Completion.create(
      engine="davinci-instruct-beta-v3",
      prompt=p,
      temperature=.7,
      max_tokens=500,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0,
      stop=["12."]
    )

# grab our text from the repsonse
text = response['choices'][0]['text']


print(text+'\n')

Sentiment Analysis #3

Sentiment analysis in a natural language processing system is an obvious must-have.

And GPT-3 does not miss.

Sentiment Analysis has millions of uses, including compiling extremely large datasets without reading each line of text manually.

Talk about a field day for a data scientist!

This can be incredibly helpful for businesses that may want to assess customer satisfaction, analyze customer feedback, or review competitor activities, among many other things – without having to read each and every comment.

The automated sentiment analysis process allows for more efficient data gathering and evaluation, resulting in a better understanding of customer needs and market trends.

Additionally, organizations can monitor how customers respond to their products or services using sentiment analysis.

Thus, it is a very useful tool that allows businesses to understand how people feel about their offerings and refine their products accordingly.

p = f'''Classify The Following Sentence As Either Nice or Mean: Dogs are Ugly'''

# generate the response
response = openai.Completion.create(
      engine="davinci-instruct-beta-v3",
      prompt=p,
      temperature=.7,
      max_tokens=500,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0,
      stop=["12."]
    )

# grab our text from the repsonse
text = response['choices'][0]['text']


print(text+'\n')

Intent Detection #4

Intent detection within an NLP system is very similar to sentiment analysis; it’s used to determine the intent of some text (Shocker).

Intent detection can be extremely beneficial when prioritizing incoming messages, such as emails and pitches.

For instance, instead of reading every email or pitch that comes in, you can use intent detection to filter out non-commercial related ones, thus allowing your team or organization to allocate its resources more efficiently.

There are millions of business use cases for intent detection; it’s a great way to create a streamlined workflow and ensure that the right messages are being sent or received.

Whether it’s lead generation or customer feedback, intent detection can help streamline the process for all types of businesses.

p = f'''Is the intent behind the following email geniune, or spam:
Hello Friend,

Congratulations, you have won a free cruise to anywhere you choose.

Please respond to this email or click here to claim your prize!'''

# generate the response
response = openai.Completion.create(
      engine="davinci-instruct-beta-v3",
      prompt=p,
      temperature=.7,
      max_tokens=500,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0,
      stop=["12."]
    )

# grab our text from the repsonse
text = response['choices'][0]['text']


print(text+'\n')

Topic Labeling #5

Topic labeling in an NLP system is useful for breaking down massive amounts of text into meaningful topics or categories.

This allows us to quickly categorize all of our data, which can be helpful when some data is more important than others.

For example, GPT-3 algorithms can help split text data into topics to identify and address the important ones compared to the others.

Topic labeling helps with text organization and furthers your ability to understand your data more deeply.

This enables researchers and developers to use their data sources better and gain an understanding beyond surface-level information.

With topic labeling in an NLP system, users can access their diverse sources of information in an organized fashion and focus on the most crucial aspects of it.

Overall, topic labeling helps users to improve their time management by quickly recognizing the essential parts of large amounts of unstructured text data.

p = f'''Classify The Following 3 Sentences into two seperate Topic Clusters that you make up and explain why:
She was once a pirate.
He was from Outerspace.
Dogs love to walk alone sometimes.'''

# generate the response
response = openai.Completion.create(
      engine="davinci-instruct-beta-v3",
      prompt=p,
      temperature=.7,
      max_tokens=500,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0,
      stop=["12."]
    )

# grab our text from the repsonse
text = response['choices'][0]['text']


print(text+'\n')

Summarization #6

Last but not least, Summarization in a natural language processing (NLP) system can be incredibly helpful in dealing with bloated text.

We know that sometimes there’s just too much text, and having something that can reduce the content can be immensely beneficial.

For example, if you were presented with ten lines about a particular topic, why go through all the trouble of reading them when you could have gotten the gist with just three?

GPT-3 is an example of a system that can help everyone save time and money by providing summarization services.

Through crafty prompts, we gain access to the point of larger articles quickly with guaranteed accuracy as the API summaries convey the same meaning as the original pieces of text.

The ability to easily summarize texts no matter how long saves valuable time and resources, making summarization a powerful tool for those working with NLP systems.

p = f'''Give me a 1 line summary of the following paragraph:
Where dogs choose to go to the bathroom is an important decision. 
It’s not just about relieving themselves, it’s about communicating with the world-at-large. 
Dogs use their urine to signal their presence to other dogs. 
And in turn, smelling other dogs’ urine tells a dog all about the other canines in the community, including their gender, age, and health. 
This system of pee-mail keeps dogs up-to-date on what’s happening in their neighborhood.'''

# generate the response
response = openai.Completion.create(
      engine="davinci-instruct-beta-v3",
      prompt=p,
      temperature=.7,
      max_tokens=500,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0,
      stop=["12."]
    )

# grab our text from the repsonse
text = response['choices'][0]['text']


print(text+'\n')

Final Thoughts & Conclusion – GPT-3 For Text Classification

Regarding text classification, GPT-3 is the perfect solution for businesses.

GPT-3 for text classification simplifies data preprocessing and ensures that structured and unstructured data are accurately classified to provide businesses with valuable insights into customer behavior.

By leveraging the six classifiers included above in this post, businesses can save time and resources when processing various data types.

This ultimately streamlines the process of gathering, interpreting, and organizing customer data for future use.