Input: headline paired with lead paragraph; no URL, source, category, publish time, image, or engagement metadata.
Fixed 80/10/10 split using StratifiedGroupKFold with seed 42.
Fine-tuning seeds: [42, 22, 202]; 3 epochs per seed.
Development Macro-F1 selects checkpoints and representative seed.
Weighted cross-entropy from training-label frequencies: True.
Effective batch size: 8; max length: 256.

Results

Metric	Mean ± sample std
Test Macro-F1	0.7044 ± 0.0104
Test accuracy	0.7193 ± 0.0117
Dev Macro-F1	0.7348 ± 0.0040

Representative seed: 22, selected only by development Macro-F1.

Per-seed

seed	dev_macro_f1	test_macro_f1	test_accuracy
22	0.7392	0.7137	0.731
42	0.7335	0.7063	0.7193
202	0.7315	0.6932	0.7076

Labels

0: non-clickbait
1: clickbait

Usage

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "BaoNhan/vibert-base-cased-ViClickbait-2025"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
title = "Tiêu đề bài báo"
lead = "Đoạn dẫn của bài báo"
inputs = tokenizer(title, lead, return_tensors="pt", truncation=True, max_length=256)
prediction = model(**inputs).logits.argmax(dim=-1).item()
print(model.config.id2label[prediction])

Dataset

Nguyen et al. (2025), ViClickbait-2025: A comprehensive dataset for Vietnamese clickbait detection. https://doi.org/10.1016/j.dib.2025.112164
Dataset: https://doi.org/10.17632/3wc46bfcjc.1

Limitations

The dataset is small, temporally bounded to 2023–2025, and collected from eight Vietnamese news platforms. Results may not transfer to social media, other publishers, or emerging clickbait styles.