bert-bullying-detector

Model Card for Davephoenix/bert-bullying-detector

A BERT-based binary classifier that detects whether a given English text contains bullying content or not. It is fine-tuned for use in moderation tools, education platforms, and social media analysis.

Model Details

Model Description

This model is based on bert-base-uncased and fine-tuned for binary text classification. The goal is to distinguish between bullying and non-bullying text, providing a tool to support online safety and moderation.

• Developed by: Davephoenix
• Funded by [optional]: Independent project
• Shared by [optional]: Davephoenix
• Model type: Text classification (binary)
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model [optional]: bert-base-uncased

Model Sources [optional]

• Repository: https://huggingface.co/Davephoenix/bert-bullying-detector
• Demo [optional]: API in progress

Uses

Direct Use

• Used for classifying short- to medium-length English text as "Bullying" or "Not Bullying".
• Can be integrated into moderation tools, educational apps, or awareness platforms.

Downstream Use [optional]

• As a building block in broader moderation or digital well-being systems.
• Further fine-tuning possible for specific platforms/domains.

Out-of-Scope Use

• Multilingual or non-English bullying detection.
• Misuse in legal or disciplinary decision-making without human oversight.
• Inference on sarcasm, coded language, or highly contextual text may be unreliable.

Bias, Risks, and Limitations

The model may exhibit limitations in:

• Cultural or contextual understanding of bullying.
• Identifying subtle or sarcastic forms of harassment.
• False positives in emotionally intense or confrontational but non-abusive language.

Recommendations

Users (both direct and downstream) should:

• Use the model alongside human review, especially in sensitive domains.
• Avoid deploying in high-stakes environments without thorough testing.
• Consider domain-specific fine-tuning if used outside general English online text.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

model_name = "Davephoenix/bert-bullying-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = F.softmax(outputs.logits, dim=1)
    pred = torch.argmax(probs, dim=1).item()
    return pred, probs[0][pred].item()

label_map = {0: "Not Bullying", 1: "Bullying"}
text = "You are so dumb and nobody likes you."
pred, confidence = classify_text(text)
print(f"Prediction: {label_map[pred]} (Confidence: {confidence:.2f})")

Training Details

Training Data

• Approximately 20,000 English text samples labeled as "bullying" or "not bullying"
• Balanced dataset curated from public moderation datasets and synthetic augmentation

Training Procedure

Preprocessing [optional]

• Tokenized using bert-base-uncased tokenizer
• Truncation and padding to max_length of 128 tokens

Training Hyperparameters

• Training regime: fp16 mixed precision
• Epochs: 3
• Batch size: 32
• Optimizer: AdamW with linear warmup
• Learning rate: 2e-5

Speeds, Sizes, Times [optional]

• Training time: ~5 hours on Kaggle GPU
• Model size: ~420MB
• Final Checkpoint: checkpoint-34371

Evaluation

Testing Data, Factors & Metrics

Testing Data

• 10% hold-out split from the training set
• Similar distribution to training data

Factors

• Sentence structure
• Presence of explicit abusive terms
• Subtlety of intent

Metrics

• Accuracy, F1 score, Loss

Results

• Accuracy: 95.6%
• F1 Score: 95.6%
• Validation Loss: 0.151

Summary

The model performs well for binary classification of bullying vs. non-bullying on general English text. Performance may degrade on ambiguous or culturally nuanced examples.

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions estimated via ML CO2 calculator:

• Hardware Type: NVIDIA P100
• Hours used: ~5
• Cloud Provider: Kaggle
• Compute Region: North America
• Carbon Emitted: < 2 kg CO₂

Technical Specifications [optional]

Model Architecture and Objective

• Architecture: BERT base uncased (12-layer, 768-hidden, 12-heads, 110M parameters)
• Objective: Binary sequence classification with cross-entropy loss

Compute Infrastructure

Hardware

• Kaggle P100 GPU (free tier)

Software

• transformers 4.39.3
• datasets 2.19.1
• Python 3.11
• PyTorch 2.x

Citation [optional]

BibTeX:

@misc{bert-bullying-detector,
  title={BERT Bullying Detector},
  author={Davephoenix},
  year={2025},
  note={Fine-tuned BERT for binary text classification (bullying detection)},
  howpublished={\url{https://huggingface.co/Davephoenix/bert-bullying-detector}}
}

APA:

Davephoenix. (2025). BERT Bullying Detector [Computer software]. Hugging Face. https://huggingface.co/Davephoenix/bert-bullying-detector

Glossary [optional]

• BERT: Bidirectional Encoder Representations from Transformers
• FP16: 16-bit floating point precision
• F1 Score: Harmonic mean of precision and recall

More Information [optional]

To request the training notebook or API wrapper, please contact the model author.

Model Card Authors [optional]

• Davephoenix

Model Card Contact

• https://huggingface.co/Davephoenix

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