AI Project: Text Generation with gpt-2 (Hugging Face)
We’ve used the Hugging Face pipeline to understand text (sentiment-analysis) and answer questions (question-answering). Now, let’s use it for its most famous task: Text Generation….
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We’ve used the Hugging Face pipeline to understand text (sentiment-analysis) and answer questions (question-answering). Now, let’s use it for its most famous task: Text Generation….

In our Polars vs. Pandas article, we showed that Polars is faster. The reason it’s faster is its Expression API. In this article, we’ll take…

We’ve used the Hugging Face pipeline to understand emotion and summarize text. Now, let’s use it to find answers. “Question-Answering” models are trained to read…

In our last AI project, we taught Python how to understand emotion. Now, let’s teach it how to read and summarize a long article for…

You’ve learned how PyScript can run Python in a browser and how to interact with the page. Now, let’s do something powerful. In this article,…

The pipeline() function in our Hugging Face intro is amazing, but it’s a black box. To do advanced work (like fine-tuning or getting raw data),…

This is the most common—and most frustrating—error when you start working with real AI models. It means: “This AI model is too big to fit…

This is a perfect first project to show the power of our Hugging Face Hub. We will use a pre-trained AI model to instantly determine…

You’ve used Pandas. You’ve read our Intro to Polars. Now, let’s answer the big question: “Why should I switch, and how hard is it?” This…

Machine learning is no longer just about Scikit-Learn. The future is Large Language Models (LLMs). Hugging Face is the “GitHub for AI models,” and their…