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": 0.8452830188679246, | |
| "recall": 0.8888888888888888, | |
| "f1-score": 0.8665377176015474, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.8502202643171806, | |
| "recall": 0.8143459915611815, | |
| "f1-score": 0.8318965517241379, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.8922413793103449, | |
| "recall": 0.8808510638297873, | |
| "f1-score": 0.8865096359743041, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.861878453038674, | |
| "macro avg": { | |
| "precision": 0.8625815541651499, | |
| "recall": 0.8613619814266192, | |
| "f1-score": 0.8616479684333299, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8621412258782042, | |
| "recall": 0.861878453038674, | |
| "f1-score": 0.8616805967516189, | |
| "support": 724.0 | |
| } | |
| } |