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Model Card for Toxicity Detection Model
This model is a fine-tuned version of bert-base-uncased for toxicity detection in English text. It has been trained on labeled datasets containing online comments categorized by their toxicity levels. The model uses the Hugging Face transformers library and is suitable for sequence classification tasks.
- Model Type: Sequence Classification
- Language(s): Turkish
- License: MIT
- Fine-tuned from:
dbmdz/bert-base-turkish-cased
Uses
This model can be used directly to analyze the toxicity of text in English. For example:
- Content moderation in online forums and social media platforms
- Filtering harmful language in customer reviews or feedback
- Monitoring and preventing cyberbullying in messaging applications
Downstream Use
- Integrating toxic language filtering into chatbots or virtual assistants
- Using it as part of a sentiment analysis pipeline
Out-of-Scope Use
- Not suitable for analyzing languages other than Turkish
- Should not be used for sensitive decision-making without human oversight
Bias, Risks, and Limitations
The model may inherit biases from the training data, including overrepresentation or underrepresentation of certain demographics or topics. It may also misclassify non-toxic content as toxic or fail to detect subtler forms of toxicity.
Recommendations
Users should:
- Avoid deploying the model in high-stakes scenarios without additional validation.
- Regularly monitor performance and update the model if new biases are detected.
Training Data
https://huggingface.co/datasets/Overfit-GM/turkish-toxic-language
Evaluation
The model was evaluated on a held-out test set containing a balanced mix of toxic and non-toxic examples.