This repository contains code to instantiate and deploy a toxic comment classifier along with a custom UI wrapper. This model is able to detect 6 types of toxicity in a text fragment. The six detectable types are toxic, severe toxic, obscene, threat, insult, and identity hate.
The model MAX Toxic Comment Classifier is based on the pre-trained BERT-Base, English Uncased model and was finetuned on the Toxic Comment Classification Dataset using the Huggingface BERT Pytorch repository.
A brief definition of the six different toxicity types can be found below.
Toxic: very bad, unpleasant, or harmful
Severe toxic: extremely bad and offensive
Obscene: (of the portrayal or description of sexual matters) offensive or disgusting by accepted standards of morality and decency
Threat: a statement of an intention to inflict pain, injury, damage, or other hostile action on someone in retribution for something done or not done
Insult: speak to or treat with disrespect or scornful abuse
Identity hate: hatred, hostility, or violence towards members of a race, ethnicity, nation, religion, gender, gender identity, sexual orientation or any other designated sector of society
| Component | License | Link |
|---|---|---|
| This repository | Apache 2.0 | LICENSE |
| Finetuned Model Weights | Apache 2.0 | LICENSE |
| Pre-trained Model Weights | Apache 2.0 | LICENSE |
| TensorFlow Model Code (3rd party) | Apache 2.0 | LICENSE |
| PyTorch Model Code (3rd party) | Apache 2.0 | LICENSE |
| Toxic Comment Classification Dataset | CC0 | LICENSE |
docker: The Docker command-line interface. Follow the installation instructions for your system.- The minimum recommended resources for this model is 4GB Memory and 4 CPUs.
TBD