Text Classification
An guide to AutoML text classification models
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 | Latency | Max Tokens | Contains Hive Classes | Use Case | Dataset Validation Requirement |
---|---|---|---|---|---|
Hive Text Classification | Low | 512 per request | No | If you want to train a text classification tool from scratch, you should use this base model. | 1. Each class requires a minimum of 10 examples. 2. Each row of text data has a maximum length restriction of 2048 characters. |
Hive Text Moderation | Low | 512 per request | Yes | If you're interested in moderation use cases, you should select this base model. This option will fine-tune our existing Hive Text Moderation model with the additional data you provide, allowing you to customize our pre-made heads or add in your own new ones. The resulting model will include all Hive Text Moderation heads as well as any you choose to create. | 1. Each class requires a minimum of 50 examples. 2. Each row of text data has a maximum length restriction of 2048 characters. |
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 these pre-made labels is shown below:
Labels | Classes |
---|---|
bullying | 0, 1, 2, 3 |
child_exploitation | 0, 3 |
child_safety | 0, 3 |
drugs | 0, 1, 2, 3 |
gibberish | 0, 3 |
hate | 0, 1, 2, 3 |
phone_number | 0, 3 |
promotions | 0, 3 |
redirection | 0, 3 |
self_harm | 0, 3 |
sexual | 0, 1, 2, 3 |
spam | 0, 3 |
violence | 0, 1, 2, 3 |
weapons | 0, 1, 2, 3 |
You can find a more detailed list of these classes and their definitions here.
Updated 1 day ago