Normal AI - Text models overview

While working with image classification is probably the most common task you’ll find, there are plenty of other more advanced possibilities as well!

Tasks and models for text analysis

Text classification

Text classification is one of the most basic yet most useful machine learning methods: it’s the process of putting pieces of text into categories.

Detecting people, places, things and more

Named entity recognition (NER) is also known as token classification or span classification. Instead of classifying an entire document, it pulls out bits and pieces you might be interested in: names, organization names, places, money, etc.

Summarization

When you want to summarize something, clearly!

Asking questions about documents

Question answering has a lot of sub-sections, but the idea in general is that you have a document and ask the model a question to be answered from it.

Document similarity

Document similarity or sentence similarity can be used to find pieces of text that are related to one another. Through a bit of magic, each document is transformed into 384 dimensions, which can be used to find ones that are about the same topics. The dimensions don’t mean anything, but you can read more about word embeddings here.

Asking PDFs questions

Asking PDFs questions actually isn’t about text, it’s about images! Except… it’s kind of about text. And layout. And images. It’s complicated.