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README.md

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+---
+language:
+- en
+- hi
+- ta
+license: apache-2.0
+tags:
+- toxicity-detection
+- multilingual
+- indicbert
+- text-classification
+- content-moderation
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+widget:
+- text: "You are kind and helpful"
+- text: "Fuck you"
+- text: "Bahut accha kiya yaar"
+- text: "Bhenchod"
+---
+
+# IndicBERT Multilingual Toxicity Detector
+
+Fine-tuned version of [ai4bharat/IndicBERTv2-MLM-only](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-only) for toxicity detection in multilingual text (English, Hinglish, Hindi, Tamil).
+
+## Model Description
+
+This model classifies text as either **toxic** or **non-toxic**. It was trained on a balanced dataset with class weights to handle imbalanced data.
+
+**Languages Supported:**
+- English
+- Hinglish (Hindi-English code-mixed)
+- Hindi
+- Tamil
+
+## Training Details
+
+- **Base Model:** ai4bharat/IndicBERTv2-MLM-only (278M parameters)
+- **Training Data:** 569 samples (balanced: 53% non-toxic, 47% toxic)
+- **Training Split:** 80/20 train/validation
+- **Epochs:** 3
+- **Batch Size:** 16
+- **Learning Rate:** 2e-5
+- **Class Weighting:** Applied to handle imbalance
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("indic-toxicity-detector")
+tokenizer = AutoTokenizer.from_pretrained("indic-toxicity-detector")
+
+# Predict
+def predict_toxicity(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+    outputs = model(**inputs)
+    probabilities = torch.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+    confidence = probabilities[0][predicted_class].item()
+    
+    label = model.config.id2label[predicted_class]
+    return {"label": label, "confidence": confidence}
+
+# Example
+result = predict_toxicity("You are amazing!")
+print(result)  # {'label': 'non-toxic', 'confidence': 0.95}
+```
+
+## Performance
+
+- **Validation Accuracy:** See training_metrics.csv
+- **F1 Score:** See training_metrics.csv
+
+## Limitations
+
+- Trained on limited dataset (569 samples)
+- May not generalize well to all types of toxic content
+- Performance varies across languages
+- Code-mixed text performance depends on training data representation
+
+## Citation
+
+```bibtex
+@misc{indic-toxicity-detector,
+  author = {Your Name},
+  title = {IndicBERT Multilingual Toxicity Detector},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/indic-toxicity-detector}
+}
+```
+
+## License
+
+Apache 2.0