Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/cafebert-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.8008298755186722, | |
| "recall": 0.7658730158730159, | |
| "f1-score": 0.7829614604462475, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.6697674418604651, | |
| "recall": 0.6075949367088608, | |
| "f1-score": 0.6371681415929203, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.6268656716417911, | |
| "recall": 0.7148936170212766, | |
| "f1-score": 0.6679920477137177, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.6975138121546961, | |
| "macro avg": { | |
| "precision": 0.6991543296736428, | |
| "recall": 0.6961205232010511, | |
| "f1-score": 0.6960405499176284, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.701460559651183, | |
| "recall": 0.6975138121546961, | |
| "f1-score": 0.6979188795617406, | |
| "support": 724.0 | |
| } | |
| } |