Text Classification
Transformers
Safetensors
Vietnamese
electra
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/velectra-base-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/velectra-base-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/velectra-base-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/velectra-base-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/velectra-base-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.8627450980392157, | |
| "recall": 0.873015873015873, | |
| "f1-score": 0.8678500986193294, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.847457627118644, | |
| "recall": 0.8438818565400844, | |
| "f1-score": 0.8456659619450317, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.8969957081545065, | |
| "recall": 0.8893617021276595, | |
| "f1-score": 0.8931623931623932, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.8687845303867403, | |
| "macro avg": { | |
| "precision": 0.8690661444374554, | |
| "recall": 0.868753143894539, | |
| "f1-score": 0.8688928179089181, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8688580300404836, | |
| "recall": 0.8687845303867403, | |
| "f1-score": 0.8688041715831574, | |
| "support": 724.0 | |
| } | |
| } |