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": 0.8404255319148937, | |
| "recall": 0.9404761904761905, | |
| "f1-score": 0.8876404494382022, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.9128440366972477, | |
| "recall": 0.8396624472573839, | |
| "f1-score": 0.8747252747252747, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.9375, | |
| "recall": 0.8936170212765957, | |
| "f1-score": 0.9150326797385621, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.8922651933701657, | |
| "macro avg": { | |
| "precision": 0.8969231895373805, | |
| "recall": 0.8912518863367233, | |
| "f1-score": 0.892466134634013, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8956405673201671, | |
| "recall": 0.8922651933701657, | |
| "f1-score": 0.8923038164459657, | |
| "support": 724.0 | |
| } | |
| } |