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.
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).
This model is intended for use in applications requiring automated toxicity detection, such as:
It can be integrated into pipelines using the Hugging Face Transformers library. Example usage:
1from transformers import pipeline
2
3classifier = pipeline("text-classification", model="your-username/toxicity-classifier")
4result = classifier("This is a harmful comment.")
5print(result)
