Publish ViClickbait-2025 best dev seed 22

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+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: 128; max length: 256.
+- BF16/TF32 and fused AdamW on NVIDIA A100 80GB.
+
+## 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.

aggregate_metrics.json

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+{
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+  "test_macro_f1_std": 0.005958654114265888,
+  "test_macro_f1_text": "0.8047 ± 0.0060",
+  "test_accuracy_mean": 0.8235867446393762,
+  "test_accuracy_std": 0.007358513094805787,
+  "test_accuracy_text": "0.8236 ± 0.0074",
+  "dev_macro_f1_mean": 0.8256067785964395,
+  "dev_macro_f1_std": 0.005270422203322432,
+  "dev_macro_f1_text": "0.8256 ± 0.0053",
+  "model_key": "cafebert",
+  "model_name": "CafeBERT",
+  "base_model": "uitnlp/CafeBERT",
+  "representative_seed": 22,
+  "selection_rule": "maximum development Macro-F1; ascending seed tie-break",
+  "seeds": [
+    42,
+    22,
+    202
+  ],
+  "input_mode": "title_plus_lead",
+  "use_class_weights": true,
+  "dataset_sha256": "a8c346648666155e97a4c0aecd5a1a01131cc94631d78965b83da3739158182e"
+}

artifacts/per_seed_results.csv

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artifacts/seed_202_classification_report.json

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artifacts/seed_202_confusion_matrix.csv

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artifacts/seed_202_run_complete.json

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