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": 0.8348214285714286, | |
| "recall": 0.7420634920634921, | |
| "f1-score": 0.7857142857142857, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.6995884773662552, | |
| "recall": 0.7172995780590717, | |
| "f1-score": 0.7083333333333334, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.8210116731517509, | |
| "recall": 0.8978723404255319, | |
| "f1-score": 0.8577235772357723, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.7845303867403315, | |
| "macro avg": { | |
| "precision": 0.7851405263631449, | |
| "recall": 0.7857451368493652, | |
| "f1-score": 0.7839237320944639, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.7860707352575469, | |
| "recall": 0.7845303867403315, | |
| "f1-score": 0.7837569622243183, | |
| "support": 724.0 | |
| } | |
| } |