---
tags:
- text-classification
- toxicity-detection
model-index:
- name: toxicity-classifier
  results: []
---

# Toxicity Classifier

## Overview

This model is a fine-tuned BERT model designed for detecting toxicity in text. It classifies input text as either "toxic" or "non-toxic" based on learned patterns from a diverse dataset of online comments and discussions. The model achieves high accuracy in identifying harmful language, making it suitable for content moderation tasks.

## Model Architecture

The model is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture, specifically `bert-base-uncased`. It consists of 12 transformer layers, each with 12 attention heads and a hidden size of 768. The final layer is a classification head that outputs probabilities for the two classes: non-toxic (0) and toxic (1).

## Intended Use

This model is intended for use in applications requiring automated toxicity detection, such as:
- Social media platforms for moderating user comments.
- Online forums to flag potentially harmful content.
- Customer support systems to identify abusive language in queries.

It can be integrated into pipelines using the Hugging Face Transformers library. Example usage:

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="your-username/toxicity-classifier")
result = classifier("This is a harmful comment.")
print(result)