Understanding AI Text Generators: How to Get Started and Beyond
Get started with super simple code, then the results will make you roll your eyes.
Do you use GPT often? Were you impressed by how quickly GPT answers questions? Or have you ever found yourself frustrated when GPT completely misunderstands you?
As a heavy GPT user, I’m often blown away by how spot-on its predictions can be. But other times, I find myself quite annoyed when GPT totally misses the mark.
Why does it feel like such an unpredictable and, at times, sassy friend to chat with?
Though frustrated, I’m not giving up this valuable brain, filled with thousands of years of collective knowledge. I decided it was time to understand my “friend” better.
In this article, I’ll start by gently diving into how AI text generation works and how we, as end-users, can incorporate it in code. Then, I’ll explore some of the most popular large language models (LLMs) and wrap it all up with a usage summary.
What Exactly is an AI Text Generator?
At its core, an AI text generator is a system trained by machine learning models to understand and produce human-like text.
These generators analyze vast amounts of text data — think books, articles, and even internet chatter — and learn the patterns and structures of language. The result? An AI capable of:
Completing your sentences (just like predictive text on your phone),
Generating entire paragraphs of coherent text,
Summarizing long documents,
Analyzing emotional tone or sentiment in text,
Providing answers based on a given passage or dataset,
Translating languages, and much more.
The magic lies in large language models (LLMs), which are massive neural networks trained on huge datasets of text. They use sophisticated architectures like transformers to process and generate text with incredible fluency.
One of the most famous LLMs? Here comes OpenAI’s GPT family.
Getting Hands-On: Hugging Face + GPT + PyTorch
If you’re eager to try your hand at building an AI text generator, there’s one unbeatable combination to get started with: Hugging Face + GPT + PyTorch.
Here’s why this combination is the go-to for many developers.
What is Hugging Face?
Hugging Face’s Transformers library is like your AI Swiss Army knife. From the model end, it simplifies access to pre-trained models like GPT-2, BART, and beyond. At the operational level, it offers tokenizers and tools to process text. Whether you’re a seasoned pro or just dipping your toes into AI, Hugging Face makes using powerful models dead simple.
What is GPT?
GPT, or Generative Pre-trained Transformer, is a series of language models created by OpenAI. GPT-2 is the most accessible of the series. It is capable of generating coherent, contextually relevant text from a given prompt. It’s great for creative writing, chatbot applications, and even summarization.
What is PyTorch?
PyTorch is a deep learning framework that powers the backend of many AI models. Think of it as the engine under the hood. It’s intuitive, flexible, and widely used in both research and production, making it the perfect partner for Hugging Face and GPT.
How They Work Together
Let’s break it down:
Hugging Face: Provides pre-trained models and tokenizers. No need to reinvent the wheel!
GPT-2: The language model that generates your text.
PyTorch: Handles the heavy lifting of running the model, processing tensors, and optimizing performance.
Here’s a simple code snippet to generate text with this combo:
from transformers import GPT2LMHeadModel, GPT2Tokenizerimport torch
# Load GPT-2 model and tokenizer from Hugging Face
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
# Prepare input text
prompt = "Once upon a time"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate text
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)Run this, and voilà! Your AI generates a continuation of the input prompt. It’s as simple as that.
Actually, don’t get too excited just yet. Sometimes, the generated results can be wildly absurd. (Note, after fixing attention_mask, the generated_text remained the same.)
This left me wondering, maybe GPT2 is not the right model?
What About Other Models?
I searched for models, and there are so many that it’s hard to tell which one is best for which task. To save the headache, I started with checking out their underlying architectures and how they compare.
Model Architectures
There are three primary architectures for LLMs:
Encoder-Only Models
Focus: Text understanding.
How They Work: Process the entire input text at once (bidirectionally), making them excellent for tasks requiring deep context comprehension.
Use Cases: Sentiment analysis, classification, and question answering.
Examples: BERT, RoBERTa.
Decoder-Only Models
Focus: Text generation.
How They Work: Process input sequentially (unidirectionally), making them ideal for predicting the next word and generating coherent text.
Use Cases: Creative writing, summarization, chatbot development.
Examples: GPT-2, GPT-3, GPT-4, LLaMA, BLOOM.
Encoder-Decoder Models
Focus: Text-to-text transformations.
How They Work: Encode the input text into a compressed representation and decode it into a different form, like summaries or translations.
Use Cases: Abstractive summarization, translation, paraphrasing.
Examples: BART, T5.
Popular LLMs: A Quick Comparison
Now it’s easy to decide, if you’re looking to generate creative content, GPT-2/3/4 and BLOOM are solid choices. For summarization, BART and T5 excel. For understanding sentiment or answering questions, BERT and RoBERTa are your best bet.
Improvement from User End
Now we know that GPT-2 excels at text generation, so those strange continuation results might not be the model’s fault after all. Instead, they’re likely due to unclear or vague input from the user.
For instance, when the input prompt lacks specificity, the output can seem incoherent or random. However, providing a well-structured and clearer prompt often produces significantly better results.
I decided to try a different prompt, “The panda picked up the apple and”? GPT-2 responds with, “The panda picked up the apple and started to eat it.”
Much more logical, right?
I’ve always understood that providing a good prompt is essential for effectively using GPT, but this experiment really drove the point home. It made a lasting impression on me:
Crafting precise and well-thought-out prompts can dramatically enhance the quality of the generated text.
Wrapping It All Up
AI text generators are opening up a world of creativity, automation, and possibilities. Whether you’re dreaming of writing the next great novel, building a chatbot, or summarizing massive documents, the right combination of tools and models can make it happen.
Key Tools to Know
Library: Hugging Face Transformers — Your one-stop shop for pre-trained models like GPT-2, BART, and more.
Frameworks: PyTorch — The engine that powers it all, with flexibility and scalability.
Models: Explore beyond GPT-2 — check out BLOOM, T5, or even LLaMA for specific needs.
So, are you ready to give it a try? Grab your laptop, download Hugging Face, and start building your very own AI text generator.
Have a different perspective or insights to share? Drop a comment below — I’d love to hear your thoughts!