YamenRM/Toxicity_model

DistilBERT Toxic Comment Classifier 🛡️

This is a DistilBERT-based binary classifier fine-tuned to detect toxic vs. non-toxic comments using the Cleaned Toxic Comments dataset.

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Model Performance

• Accuracy: ~94%
• Class metrics:
  • Non-toxic (0): Precision 0.96 | Recall 0.95 | F1 0.95
  • Toxic (1): Precision 0.90 | Recall 0.91 | F1 0.91

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Dataset

• Name: Cleaned Toxic Comments (FizzBuzz @ Kaggle)
• Language: English
• Classes:
  • 0 = Non-toxic
  • 1 = Toxic
• Balancing: To reduce class imbalance, undersampling was applied to the majority (non-toxic) class.

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Training Details

Hyperparameter	Value
Base model	distilbert-base-uncased
Epochs	3
Batch size	32
Learning rate	2e-5
Loss function	CrossEntropyLoss (with undersampling)

• Optimizer: AdamW
• Framework: Hugging Face Transformers
• Hardware: Google Colab GPU

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How to Use

Load with the Hugging Face pipeline:

from transformers import pipeline

classifier = pipeline("text-classification", model="YamenRM/distilbert-toxic-comments")

print(classifier("I hate everyone, you're the worst!"))
# [{'label': 'toxic', 'score': 0.97}]

Considerations

Because of undersampling of non-toxic comments, the model might be less robust on very large, unbalanced datasets in real-world settings.

If Toxic content is very rare in your target domain, the model might produce more false positives or negatives than expected.

This model is trained only in English — performance may drop for non-English or mixed-language texts.

Acknowledgements & License

Thanks to the Kaggle community for sharing the Cleaned Toxic Comments dataset.

Built using Hugging Face’s transformers & datasets libraries.

License: [Apache-2.0]

Contact & Feedback

If you find issues, want improvements (e.g. support for other languages, finer toxicity categories), or want to collaborate, feel free to open an issue or contact me at yamenrafat132@gmail.com.