Text Classification
Transformers
Safetensors
Vietnamese
distilbert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/distilbert-multilingual-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/distilbert-multilingual-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/distilbert-multilingual-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.717948717948718, | |
| "recall": 0.7777777777777778, | |
| "f1-score": 0.7466666666666667, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.6291666666666667, | |
| "recall": 0.6371308016877637, | |
| "f1-score": 0.6331236897274634, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.8720379146919431, | |
| "recall": 0.7829787234042553, | |
| "f1-score": 0.8251121076233184, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.7334254143646409, | |
| "macro avg": { | |
| "precision": 0.7397177664357759, | |
| "recall": 0.7326291009565988, | |
| "f1-score": 0.7349674880058162, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.7389012249664137, | |
| "recall": 0.7334254143646409, | |
| "f1-score": 0.734960856017802, | |
| "support": 724.0 | |
| } | |
| } |