TaiwoOgun/minilm-hate-speech-onnx

MiniLM Hate Speech Detector

Fine-tuned MiniLM-L12 model for binary hate speech detection, optimized for browser deployment.

Model Description

This model classifies text as either hate speech or not hate speech (binary classification). It was fine-tuned on the HateXplain dataset and exported to ONNX format with INT8 quantization for efficient on-device inference in browser extensions.

Training Data

• Dataset: HateXplain (~20K samples)
• Sources: Twitter, Gab
• Label Mapping:
  • hate (1): Hate speech
  • not_hate (0): Offensive + Normal content merged

Evaluation Results

Model Comparison (RQ3)

Intended Use

• Primary: Browser extension for real-time hate speech detection
• Framework: Transformers.js for on-device inference
• Privacy: 100% client-side processing, no data sent to servers

Usage

With Transformers.js (Browser)

import { pipeline } from '@huggingface/transformers';

const classifier = await pipeline(
  'text-classification',
  'TaiwoOgun/minilm-hate-speech-onnx'
);

const result = await classifier('Some text to classify');
console.log(result);
// [{ label: 'hate', score: 0.85 }]

With Python

from transformers import pipeline

classifier = pipeline('text-classification', 'TaiwoOgun/minilm-hate-speech')
result = classifier('Some text to classify')

Limitations

• Language: English only
• Binary: Does not distinguish between types of hate speech
• Context: May miss context-dependent hate speech or sarcasm
• Dataset Bias: Trained on Twitter/Gab data, may not generalize to all platforms

Citation

If you use this model, please cite:

@misc{ogunbanwo2025hatespeech,
  author = {Ogunbanwo, Taiwo},
  title = {Real-Time Hate Speech Detection Browser Extension},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/TaiwoOgun/minilm-hate-speech-onnx}
}

License

MIT License