---
language:
  - bn
license: mit
tags:
  - text-classification
  - multi-label-classification
  - bangla
  - cyberbullying
  - bert
  - pytorch
datasets:
  - custom
metrics:
  - f1
  - accuracy
pipeline_tag: text-classification
---

# Bangla Cyberbullying Detection Model

This model is fine-tuned for multi-label classification to detect cyberbullying in Bangla text.

## Model Details

- **Base Model:** sagorsarker/bangla-bert-base
- **Task:** Multi-label text classification
- **Labels:** bully, sexual, religious, threat, spam
- **Number of Labels:** 5
- **Classifier Hidden Size:** 256
- **Dropout:** 0.1

## Usage

### Installation

```bash
pip install torch transformers
```

### Loading and Inference

```python
from model import TransformerMultiLabelClassifier
from transformers import AutoTokenizer
import torch

# Load the model
model = TransformerMultiLabelClassifier.from_pretrained("path/to/saved/model")
tokenizer = AutoTokenizer.from_pretrained("path/to/saved/model")

# Prepare input
text = "আপনার বাংলা টেক্সট এখানে"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)

# Get predictions
outputs = model.predict(inputs['input_ids'], inputs['attention_mask'])
    
probabilities = outputs['probabilities'][0]
predictions = outputs['predictions'][0]

labels = ['bully', 'sexual', 'religious', 'threat', 'spam']
for label, prob, pred in zip(labels, probabilities, predictions):
    status = "✓ Detected" if pred else "✗ Not detected"
    print(f"{label}: {prob:.4f} ({status})")
```

### Using with Pipeline (Alternative)

```python
# For batch inference
texts = ["টেক্সট ১", "টেক্সট ২", "টেক্সট ৩"]
inputs = tokenizer(texts, return_tensors="pt", truncation=True, padding=True, max_length=128)
outputs = model.predict(inputs['input_ids'], inputs['attention_mask'])
```

## Labels

| Label | Description |
|-------|-------------|
| bully | General bullying content |
| sexual | Sexual harassment or inappropriate content |
| religious | Religious hate or discrimination |
| threat | Threatening content |
| spam | Spam or irrelevant content |

## Training

This model was trained using:
- K-fold cross-validation with multi-label stratification
- AdamW optimizer with linear warmup
- Mixed precision training (AMP)
- Early stopping based on weighted F1 score

## Citation

If you use this model, please cite:

```bibtex
@misc{bangla-cyberbullying-detection,
  author = {Your Name},
  title = {Bangla Cyberbullying Detection Model},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/your-username/your-model}
}
```

## Limitations

- Trained specifically on Bangla text
- Performance may vary on out-of-domain text
- Multi-label threshold of 0.5 used by default (can be adjusted)
- May not generalize well to code-mixed text (Bangla + English)