Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
clickbait-detection
vietnamese
viclickbait-2025
text-embeddings-inference
Instructions to use BaoNhan/cafebert-ViClickbait-2025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViClickbait-2025 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViClickbait-2025")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViClickbait-2025") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViClickbait-2025", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_macro_f1_mean": 0.8047001602209618, | |
| "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" | |
| } |