Text Classification

Overview

Text classification models allow you to predict a category or class for a piece of text content. Popular uses for text classification models include content moderation, topic labelling, sentiment analysis, and more.

Which Base Model Should I Use?

Base Model	Max Tokens	Use Case	Hive Classes	Dataset Requirements
Text Classification v2	512	Well-suited for most text classification tasks	--	Minimum 10 examples per class
Text Moderation v2	512	Best for moderation tasks—leverages pre-trained Hive moderation labels	Included	Minimum 50 examples per class
DeBERTa v3	512	Best for sentiment analysis or very complex/nuanced classification	--	Minimum 10 examples per class
Longformer v1	1024	Best for long-form text inputs that are too long for other models	--	Minimum 10 examples per class

Hive Text Moderation Labels and Classes

If you select the Hive Text Moderation base model, the model output will always contain all Hive Text Moderation categories. A full list of moderation labels can be found in the Text Moderation documentation.