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": 0.8455882352941176, | |
| "recall": 0.9126984126984127, | |
| "f1-score": 0.8778625954198473, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.9036697247706422, | |
| "recall": 0.8312236286919831, | |
| "f1-score": 0.865934065934066, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.9017094017094017, | |
| "recall": 0.8978723404255319, | |
| "f1-score": 0.8997867803837953, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.8812154696132597, | |
| "macro avg": { | |
| "precision": 0.8836557872580538, | |
| "recall": 0.880598127271976, | |
| "f1-score": 0.8811944805792362, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8828172230199852, | |
| "recall": 0.8812154696132597, | |
| "f1-score": 0.8810740898651478, | |
| "support": 724.0 | |
| } | |
| } |