Text Classification
Transformers
Safetensors
Vietnamese
bert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/wikibert-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/wikibert-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/wikibert-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/wikibert-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/wikibert-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.8122448979591836, | |
| "recall": 0.7896825396825397, | |
| "f1-score": 0.8008048289738431, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.7336244541484717, | |
| "recall": 0.7088607594936709, | |
| "f1-score": 0.721030042918455, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.836, | |
| "recall": 0.8893617021276595, | |
| "f1-score": 0.8618556701030928, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.7955801104972375, | |
| "macro avg": { | |
| "precision": 0.7939564507025517, | |
| "recall": 0.7959683337679567, | |
| "f1-score": 0.7945635139984636, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.7942192125951687, | |
| "recall": 0.7955801104972375, | |
| "f1-score": 0.7945069330763939, | |
| "support": 724.0 | |
| } | |
| } |