Text Generation

Text generation is one of the most exciting applications of modern NLP.

It allows machines to produce coherent, context-aware text, stories, summaries, translations, emails, and even code. Thanks to Transformer-based models like GPT-2, GPT-Neo, and flan-T5, among others, text generation has become extremely accessible in Python through the Hugging Face pipeline API.

In this lesson, you’ll explore how text generation works, how to control its behavior, and how to use different models for different tasks. By the end, you’ll be comfortable writing your own generation scripts and experimenting with creativity, coherence, and task-specific outputs.

What is text generation?

Text generation refers to producing new text based on a given input prompt.

The model analyzes the context and predicts the next most likely words, repeating this process until it completes the output. Modern generative models are trained on massive datasets, enabling them to produce human-like text that is fluent, creative, and contextually appropriate.

Transformers revolutionized this field by processing text in parallel and understanding word relationships using self-attention. Because of this, even small models, such as GPT-2, perform impressively on creative writing tasks, while larger, instruction-tuned models like flan-T5 excel at structured tasks, including summarization, translation, and Q&A.

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