This model is MYBully-CyBERT, fine-tuned on a combination of manually annotated data + Human-in-the-Loop (HITL) annotated data from the MYBully dataset.
It extends the manual baseline with additional annotations validated via HITL.

Intended Use

Cyberbullying detection in Bahasa Malaysia tweets.
Research on the impact of HITL annotation for low-resource abusive language tasks.

Training Data

Dataset: MYBully (Bahasa Malaysia tweets).
Annotation: Manual + HITL

Model Details

Base model: roberta-base-bahasa-cased
Fine-tuning: Binary classification head
Labels: Cyberbully (1) vs Non-cyberbully (0)

Performance

Metric	Value
Accuracy	0.85
Precision	0.86
Recall	0.89
F1	0.88

bibtex

@misc{mohan_raj_2026,
	author       = { Mohan Raj and Manjeevan Singh Seera },
	title        = { mybully-cybert-manual-hitl},
	year         = 2026,
	url          = { https://huggingface.co/mohanrj/mybully-cybert-manual-hitl },
	doi          = { 10.57967/hf/8182 },
	publisher    = { Hugging Face },
    howpublished = {\url{https://huggingface.co/mohanrj/mybully-cybert-manual-hitl}}
}