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
| { | |
| "non-clickbait": { | |
| "precision": 0.9205607476635514, | |
| "recall": 0.8277310924369747, | |
| "f1-score": 0.8716814159292036, | |
| "support": 238.0 | |
| }, | |
| "clickbait": { | |
| "precision": 0.6796875, | |
| "recall": 0.8365384615384616, | |
| "f1-score": 0.75, | |
| "support": 104.0 | |
| }, | |
| "accuracy": 0.8304093567251462, | |
| "macro avg": { | |
| "precision": 0.8001241238317758, | |
| "recall": 0.8321347769877181, | |
| "f1-score": 0.8108407079646018, | |
| "support": 342.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8473127425260973, | |
| "recall": 0.8304093567251462, | |
| "f1-score": 0.8346788800910832, | |
| "support": 342.0 | |
| } | |
| } |