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--- |
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tags: |
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- text-classification |
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- toxicity-detection |
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model-index: |
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- name: toxicity-classifier |
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results: [] |
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--- |
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# Toxicity Classifier |
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## Overview |
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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. |
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## Model Architecture |
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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). |
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## Intended Use |
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This model is intended for use in applications requiring automated toxicity detection, such as: |
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- Social media platforms for moderating user comments. |
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- Online forums to flag potentially harmful content. |
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- Customer support systems to identify abusive language in queries. |
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It can be integrated into pipelines using the Hugging Face Transformers library. Example usage: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="your-username/toxicity-classifier") |
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result = classifier("This is a harmful comment.") |
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print(result) |
