Text Classification
Transformers
Safetensors
Vietnamese
distilbert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/distilbert-multilingual-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/distilbert-multilingual-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/distilbert-multilingual-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.7623318385650224, | |
| "recall": 0.6746031746031746, | |
| "f1-score": 0.7157894736842105, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.5217391304347826, | |
| "recall": 0.5063291139240507, | |
| "f1-score": 0.5139186295503212, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.5239852398523985, | |
| "recall": 0.6042553191489362, | |
| "f1-score": 0.5612648221343873, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.5966850828729282, | |
| "macro avg": { | |
| "precision": 0.6026854029507346, | |
| "recall": 0.5950625358920538, | |
| "f1-score": 0.5969909751229729, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.6062103986142856, | |
| "recall": 0.5966850828729282, | |
| "f1-score": 0.5995509610130224, | |
| "support": 724.0 | |
| } | |
| } |