Let’s Build GPT from Scratch for Text Generator

Author(s): Asad iqbal

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

We will use KerasNLP to build a scaled-down Generative Pre-Trained (GPT) model in this example. GPT is a Transformer-based model that allows you to generate sophisticated text from a prompt.

We will train the model on the simplebooks-92 corpus, a dataset made from several novels. It is a good dataset for this example because it has a small vocabulary and high word frequency, which is beneficial when training a model with few parameters.

◆ Introduction◆ Setup◆ Settings & hyperparameters◆ Load the data◆ Train the tokenizer◆ Load tokenizer◆ Tokenize data◆ Build the model◆ Training◆ Inference -> Greedy search -> Beam search -> Random search◆ Conclusion

Source: The code used in this article is from Keras official website

Note: If you are running this example on a Colab, make sure to enable GPU runtime for faster training.

The command !pip install -q –upgrade keras-nlp installs and upgrades the keras-nlp package. The -q flag ensures the installation process is quiet (minimal output), and –upgrade provides the latest version of the package is installed.

The command !pip install -q –upgrade keras upgrades the keras package to the latest version.

!pip install -q –upgrade keras-nlp!pip install -q –upgrade… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI