| --- |
| language: vi |
| library_name: transformers |
| pipeline_tag: text-classification |
| tags: |
| - clickbait-detection |
| - vietnamese |
| - viclickbait-2025 |
| datasets: |
| - ViClickbait-2025 |
| --- |
| |
| # CafeBERT — ViClickbait-2025 |
|
|
| Fine-tuned from `uitnlp/CafeBERT` for binary Vietnamese clickbait detection. |
|
|
| ## Experimental setup |
|
|
| - 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 | 0.8047 ± 0.0060 | |
| | Test accuracy | 0.8236 ± 0.0074 | |
| | Dev Macro-F1 | 0.8256 ± 0.0053 | |
|
|
| Representative seed: **22**, selected only by development Macro-F1. |
|
|
| ### Per-seed |
|
|
| | seed | dev_macro_f1 | test_macro_f1 | test_accuracy | |
| |-------:|---------------:|----------------:|----------------:| |
| | 22 | 0.8312 | 0.8043 | 0.8246 | |
| | 42 | 0.8249 | 0.7989 | 0.8158 | |
| | 202 | 0.8207 | 0.8108 | 0.8304 | |
| |
| ## Labels |
| |
| - `0`: non-clickbait |
| - `1`: clickbait |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| |
| model_id = "BaoNhan/cafebert-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. |
| |
