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--- |
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license: mit |
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datasets: |
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- Overfit-GM/turkish-toxic-language |
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language: |
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- tr |
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base_model: |
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- dbmdz/bert-base-turkish-cased |
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pipeline_tag: text-classification |
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library_name: transformers |
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tags: |
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- text-classification |
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- toxicity-detection |
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- turkish |
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- bert |
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- nlp |
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- content-moderation |
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--- |
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# MeowML/ToxicBERT - Turkish Toxic Language Detection |
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## Model Description |
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ToxicBERT is a fine-tuned BERT model specifically designed for detecting toxic language in Turkish text. Built upon the `dbmdz/bert-base-turkish-cased` foundation model, this classifier can identify potentially harmful, offensive, or toxic content in Turkish social media posts, comments, and general text. |
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## Model Details |
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- **Model Type**: Text Classification (Binary) |
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- **Language**: Turkish (tr) |
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- **Base Model**: `dbmdz/bert-base-turkish-cased` |
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- **License**: MIT |
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- **Library**: Transformers |
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- **Task**: Toxicity Detection |
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## Intended Use |
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### Primary Use Cases |
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- Content moderation for Turkish social media platforms |
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- Automated filtering of user-generated content |
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- Research in Turkish NLP and toxicity detection |
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- Educational purposes for understanding toxic language patterns |
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### Out-of-Scope Use |
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- This model should not be used as the sole decision-maker for content moderation without human oversight |
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- Not suitable for languages other than Turkish |
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- Should not be used for sensitive applications without proper validation and testing |
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## Training Data |
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The model was trained on the `Overfit-GM/turkish-toxic-language` dataset, which contains Turkish text samples labeled for toxicity. The dataset includes various forms of toxic content commonly found in online Turkish communications. |
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## Model Performance |
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The model outputs: |
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- **Binary Classification**: 0 (Non-toxic) or 1 (Toxic) |
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- **Confidence Score**: Probability score indicating model confidence |
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- **Toxic Probability**: Specific probability of the text being toxic |
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## Usage |
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### Quick Start |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-cased") |
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model = AutoModelForSequenceClassification.from_pretrained("MeowML/ToxicBERT") |
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# Prepare text |
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text = "Merhaba, nasılsın?" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256) |
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# Get prediction |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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prediction = torch.argmax(probabilities, dim=-1) |
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toxic_probability = probabilities[0][1].item() |
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is_toxic = bool(prediction.item()) |
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print(f"Is toxic: {is_toxic}") |
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print(f"Toxic probability: {toxic_probability:.4f}") |
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``` |
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### Advanced Usage with Custom Class |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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class ToxicLanguageDetector: |
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def __init__(self, model_name="MeowML/ToxicBERT"): |
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self.tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-cased") |
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self.model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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self.model.to(self.device) |
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self.model.eval() |
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def predict(self, text): |
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inputs = self.tokenizer( |
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text, |
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truncation=True, |
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padding='max_length', |
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max_length=256, |
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return_tensors='pt' |
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).to(self.device) |
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with torch.no_grad(): |
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outputs = self.model(**inputs) |
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probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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prediction = torch.argmax(probabilities, dim=-1) |
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return { |
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'text': text, |
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'is_toxic': bool(prediction.item()), |
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'toxic_probability': probabilities[0][1].item(), |
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'confidence': max(probabilities[0]).item() |
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} |
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# Usage |
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detector = ToxicLanguageDetector() |
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result = detector.predict("Merhaba, nasılsın?") |
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print(result) |
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``` |
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## Limitations and Biases |
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### Limitations |
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- The model's performance depends heavily on the training data quality and coverage |
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- May have difficulty with context-dependent toxicity (sarcasm, irony) |
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- Performance may vary across different Turkish dialects or informal language |
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- Shorter texts might be more challenging to classify accurately |
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### Potential Biases |
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- The model may reflect biases present in the training dataset |
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- Certain topics, demographics, or linguistic patterns might be over- or under-represented |
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- Regular evaluation and bias testing are recommended for production use |
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## Ethical Considerations |
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- This model should be used responsibly with human oversight |
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- False positives and negatives are expected and should be accounted for |
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- Consider the impact on freedom of expression when implementing automated moderation |
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- Regular auditing and updating are recommended to maintain fairness |
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## Technical Specifications |
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- **Input**: Text strings (max 256 tokens) |
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- **Output**: Binary classification with probability scores |
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- **Model Size**: Based on BERT-base architecture |
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- **Inference Speed**: Optimized for both CPU and GPU inference |
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- **Memory Requirements**: Suitable for standard hardware configurations |
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## Citation |
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If you use this model in your research or applications, please cite: |
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```bibtex |
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@misc{meowml_toxicbert_2024, |
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title={ToxicBERT: Turkish Toxic Language Detection}, |
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author={MeowML}, |
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year={2024}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/MeowML/ToxicBERT} |
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} |
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``` |
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## Acknowledgments |
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- Base model: `dbmdz/bert-base-turkish-cased` |
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- Training dataset: `Overfit-GM/turkish-toxic-language` |
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- Built with Hugging Face Transformers library |
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## Contact |
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For questions, issues, or suggestions, please open an issue in the model repository or contact the MeowML team. |
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--- |
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**Disclaimer**: This model is provided for research and educational purposes. Users are responsible for ensuring appropriate and ethical use in their applications. |
