reading writing arithmetic
Building a Small Language Model: Tools for Read, Write, & Arithmetic
Artificial intelligence (AI) has revolutionized how we interact with information. At its core, an AI trained on language needs three fundamental skills:
Read: The ability to consume and understand large volumes of text.
Write: The ability to generate its own text, answer questions, or translate languages.
Arithmetic: The internal "thinking" done by the model; the calculations and parameter adjustments that enable it to process language.
This blog post kicks off a series exploring the process of building a small language model. We'll discuss the essential tools and platforms to get you started, and today we're delving into the cornerstone of AI: reading.
The Importance of the Right Data
A language model's ability to communicate intelligently is directly shaped by the data it learns from. Choosing the right data and equipping your model with powerful reading tools is crucial to its success. Questions to consider include:
What's the Goal? General-purpose language models have different needs than one specialized for finance or scientific literature. Target your data sources accordingly.
Structured vs. Unstructured: Can your model only handle neatly formatted text, or will it need to tackle blogs, social media, or even PDFs?
Quality over Quantity (at first): An initial focus on curated, high-quality data allows your model to learn the fundamentals of language before tackling the messiness of the broader internet.
Tools for the Reading Task
Here are some categories of tools that will become your language model's toolkit:
Web Scrapers: If building a general-purpose model, these help extract targeted text from websites (respecting terms of service and copyright).
PDF Readers: Especially important for specialized domains like finance, where reports are often in PDF formats. Tools like Adobe Acrobat Pro and open-source options like Tesseract OCR have varying levels of complexity and features.
Data Cleaning and Preprocessing: Before feeding data to your model, you'll often need to clean it up and transform it into structured formats. Python libraries like Pandas become your best friend for these tasks.
Key Takeaways
Starting with a well-defined aim for your language model informs your data collection and tool choices.
Building strong reading capability involves more than just finding text – presentation format and data cleaning are vital for your model to actually learn from what it reads
It's a balancing act. Start with targeted, high-quality data but think early about the adaptability of your reading tools for handling diverse sources down the line.
