Update README.md

README.md

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