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toxic-bert

Maintainer: unitary

Total Score

116

Last updated 5/15/2024

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The toxic-bert model is a Transformer-based model trained using PyTorch Lightning and Transformers to classify toxic comments. It was developed by Unitary, an AI company working to stop harmful content online. The model was trained on several Jigsaw datasets, including the Toxic Comment Classification Challenge, the Jigsaw Unintended Bias in Toxicity Classification, and the Jigsaw Multilingual Toxic Comment Classification.

The toxic-bert model is similar to other BERT-based models, such as mDeBERTa-v3-base-xnli-multilingual-nli-2mil7, which can also perform text classification tasks. However, the toxic-bert model is specifically tuned for detecting different types of toxicity, such as threats, obscenity, insults, and identity-based hate. It also aims to minimize unintended bias with respect to mentions of identities.

Model inputs and outputs

Inputs

  • Text sequences: The toxic-bert model takes in text sequences, such as comments or reviews, that need to be classified for toxicity.

Outputs

  • Toxicity predictions: The model outputs predictions for different types of toxicity, including threats, obscenity, insults, and identity-based hate. It provides a score for each type of toxicity, indicating the likelihood that the input text exhibits that particular form of toxicity.

Capabilities

The toxic-bert model is capable of accurately detecting various forms of toxicity in text, even in multilingual settings. It was trained on data from several Jigsaw datasets, which cover a wide range of toxic content and languages. The model can be used to moderate online comments, reviews, or other user-generated content, helping to create safer and more inclusive online communities.

What can I use it for?

The toxic-bert model can be used in a variety of applications that require the detection and moderation of toxic content. Some potential use cases include:

  • Online community moderation: Integrating the model into comment sections or forums to automatically flag and filter out toxic comments.
  • Content monitoring and filtering: Applying the model to review user-generated content, such as social media posts or product reviews, to identify and remove harmful content.
  • Toxic content analysis: Leveraging the model's insights to better understand the types of toxicity present in a dataset or online community, which can inform content policies and moderation strategies.

Things to try

One interesting aspect of the toxic-bert model is its ability to detect unintended bias in toxicity classification. By training on the Jigsaw Unintended Bias in Toxicity Classification dataset, the model learns to recognize and mitigate biases with respect to mentions of identities. Developers can experiment with this capability by testing the model's performance on datasets that are designed to assess bias, such as the WaNLI dataset.

Another intriguing feature of the toxic-bert model is its multilingual capabilities. Since it was trained on datasets covering multiple languages, it can be used to detect toxicity in a wide range of languages. Developers can explore the model's performance on non-English text by testing it on the Jigsaw Multilingual Toxic Comment Classification dataset.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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