Domain Classifier

Model Overview

This is a text classification model to classify documents into one of 26 domain classes:

'Adult', 'Arts_and_Entertainment', 'Autos_and_Vehicles', 'Beauty_and_Fitness', 'Books_and_Literature', 'Business_and_Industrial', 'Computers_and_Electronics', 'Finance', 'Food_and_Drink', 'Games', 'Health', 'Hobbies_and_Leisure', 'Home_and_Garden', 'Internet_and_Telecom', 'Jobs_and_Education', 'Law_and_Government', 'News', 'Online_Communities', 'People_and_Society', 'Pets_and_Animals', 'Real_Estate', 'Science', 'Sensitive_Subjects', 'Shopping', 'Sports', 'Travel_and_Transportation'

Model Architecture

The model architecture is Deberta V3 Base Context length is 512 tokens

Training (details)

Training data:

• 1 million Common Crawl samples, labeled using Google Cloud’s Natural Language API: https://cloud.google.com/natural-language/docs/classifying-text
• 500k Wikepedia articles, curated using Wikipedia-API: https://pypi.org/project/Wikipedia-API/

Training steps:

Model was trained in multiple rounds using Wikipedia and Common Crawl data, labeled by a combination of pseudo labels and Google Cloud API.

How To Use This Model

Input

The model takes one or several paragraphs of text as input. Example input:

q Directions
1. Mix 2 flours and baking powder together
2. Mix water and egg in a separate bowl. Add dry to wet little by little
3. Heat frying pan on medium
4. Pour batter into pan and then put blueberries on top before flipping
5. Top with desired toppings!

Output

The model outputs one of the 26 domain classes as the predicted domain for each input sample. Example output:

Food_and_Drink

How to use in NeMo Curator

The inference code is available on NeMo Curator's GitHub repository. Download the model.pth file and check out this example notebook to get started.

Evaluation Benchmarks

Evaluation Metric: PR-AUC PR-AUC score on evaluation set with 105k samples - 0.9873 PR-AUC score for each domain:

References

• https://arxiv.org/abs/2111.09543
• https://github.com/microsoft/DeBERTa

License

License to use this model is covered by the Apache 2.0. By downloading the public and release version of the model, you accept the terms and conditions of the Apache License 2.0. This repository contains the code for the domain classifier model.