Text Classification
Transformers
Safetensors
Vietnamese
bert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/wikibert-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/wikibert-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/wikibert-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/wikibert-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/wikibert-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.755656108597285, | |
| "recall": 0.6626984126984127, | |
| "f1-score": 0.7061310782241015, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.528957528957529, | |
| "recall": 0.5780590717299579, | |
| "f1-score": 0.5524193548387096, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.5942622950819673, | |
| "recall": 0.6170212765957447, | |
| "f1-score": 0.605427974947808, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.6201657458563536, | |
| "macro avg": { | |
| "precision": 0.6262919775455938, | |
| "recall": 0.6192595870080385, | |
| "f1-score": 0.6213261360035397, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.62906065341673, | |
| "recall": 0.6201657458563536, | |
| "f1-score": 0.6231270620469372, | |
| "support": 724.0 | |
| } | |
| } |