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Applied Text Generation using GPT and KerasNLP in Python

Text generation has gained immense popularity in recent years, thanks to advances in natural language processing (NLP) and machine learning. One of the most remarkable developments in this field is the Generative Pre-trained Transformer (GPT) architecture. In this article, we will explore how to apply text generation using GPT and KerasNLP in Python.

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Introduction to GPT

The Generative Pre-trained Transformer, or GPT, is a state-of-the-art NLP model developed by OpenAI. GPT is built upon the transformer architecture and is pre-trained on a massive amount of text data. It has the ability to generate coherent and contextually relevant text, making it an invaluable tool for various NLP tasks, including text generation.

GPT models are known for their impressive performance in tasks such as language modeling, text completion, and even creative text generation, such as story writing and poetry. In this tutorial, we will focus on using GPT-3, one of the latest and most powerful iterations of the GPT series.

Setting up the Environment

Before we dive into text generation, we need to set up our environment. We'll be using Python for this tutorial, along with the KerasNLP library to interface with the GPT-3 model. Make sure you have Python installed on your system, and then install KerasNLP using pip:

pip install kerasnlp

Next, you'll need an API key from OpenAI to access the GPT-3 model. You can obtain one by signing up on the OpenAI platform and following their instructions.

Text Generation with GPT and KerasNLP

Now that we have our environment set up, let's proceed with text generation using GPT and KerasNLP. We'll start by importing the necessary libraries and initializing our GPT model.

import kerasnlp from kerasnlp.gpt import GPT # Replace 'YOUR_API_KEY' with your actual OpenAI API key api_key = 'YOUR_API_KEY' # Initialize the GPT model gpt = GPT(api_key=api_key, model="gpt3.5-turbo")

With our GPT model initialized, we can now generate text. Let's start with a simple example:

# Generate text prompt = "Once upon a time" generated_text = gpt.generate(prompt, max_tokens=50) print(generated_text)

In this example, we provided a prompt, "Once upon a time," and requested the model to generate 50 tokens of text. The generated text will be a continuation of the prompt, creating a coherent story.

Customizing Text Generation

GPT allows us to customize text generation by adjusting various parameters. Here are some common options:


The temperature parameter controls the randomness of the generated text. A higher temperature (e.g., 0.8) produces more diverse and creative text, while a lower temperature (e.g., 0.2) makes the text more deterministic and focused.

# Generate text with a higher temperature generated_text = gpt.generate(prompt, max_tokens=50, temperature=0.8) # Generate text with a lower temperature generated_text = gpt.generate(prompt, max_tokens=50, temperature=0.2)

Max Tokens

The max_tokens parameter limits the length of the generated text. You can set it to a specific number to control the text's length.

# Generate text with a maximum of 100 tokens generated_text = gpt.generate(prompt, max_tokens=100)


GPT models come in different versions or "engines." The choice of engine affects the response time and cost. The "gpt3.5-turbo" engine is recommended for most use cases due to its balance between performance and cost.

# Initialize a different GPT engine gpt = GPT(api_key=api_key, model="gpt3.5-turbo")

Prompt Engineering

The quality of the prompt greatly influences the generated text. Experiment with different prompts to achieve the desired results. You can also provide context or instructions within the prompt to guide the model.

Practical Applications

Text generation using GPT has a wide range of practical applications:

Content Generation

GPT can generate articles, blog posts, product descriptions, and other forms of content automatically. This can be a valuable tool for content creators and marketers.

prompt = "Write a 500-word article about artificial intelligence." generated_text = gpt.generate(prompt, max_tokens=500)

Creative Writing

GPT can be used for creative writing, such as generating poetry, short stories, or dialogues.

prompt = "Write a poem about the beauty of nature." generated_text = gpt.generate(prompt, max_tokens=100)


GPT-powered chatbots can provide natural and engaging interactions with users. They can answer questions, provide recommendations, and hold conversations.

user_input = "Tell me a joke!" generated_response = gpt.generate(user_input, max_tokens=20)

Language Translation

GPT can be used for language translation by providing a sentence in one language as input and requesting the translation in another language.

input_text = "Hello, how are you?" generated_translation = gpt.generate(input_text, max_tokens=20, target_language="fr")

Ethical Considerations

While GPT is a powerful tool, it's essential to use it responsibly and consider ethical implications. Here are a few things to keep in mind:


GPT models may exhibit bias based on the training data. Be cautious when generating text on sensitive topics and review the output for bias or offensive content.


Generated text should not be presented as factual information unless verified. GPT can generate plausible-sounding but incorrect information.


Avoid generating text that may violate privacy or reveal sensitive personal information.


Text generation using GPT and KerasNLP in Python opens up exciting possibilities for automation, creativity, and user engagement. By customizing prompts and parameters, you can harness the power of GPT for a wide range of applications. However, it's crucial to use this technology responsibly, considering ethical and legal considerations. With the right approach, GPT can be a valuable addition to your NLP toolbox, revolutionizing the way you create and interact with text.

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