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Saif-Siddique/bangla-cyberbully-google-bert-base-multilingual-cased

sourceHugging Facemitupdated 9mo agoView on Hugging Face

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Bangla Cyberbullying Detection Model

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

Model Details

—Base Model: google-bert/bert-base-multilingual-cased
—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)