Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/cafebert-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.8162544169611308, | |
| "recall": 0.9166666666666666, | |
| "f1-score": 0.8635514018691589, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.9132653061224489, | |
| "recall": 0.7552742616033755, | |
| "f1-score": 0.8267898383371824, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.8857142857142857, | |
| "recall": 0.9234042553191489, | |
| "f1-score": 0.9041666666666667, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.8660220994475138, | |
| "macro avg": { | |
| "precision": 0.8717446695992885, | |
| "recall": 0.865115061196397, | |
| "f1-score": 0.8648359689576693, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8705564195691746, | |
| "recall": 0.8660220994475138, | |
| "f1-score": 0.8647007066624405, | |
| "support": 724.0 | |
| } | |
| } |