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": 0.7412280701754386, | |
| "recall": 0.6706349206349206, | |
| "f1-score": 0.7041666666666667, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.5681818181818182, | |
| "recall": 0.5274261603375527, | |
| "f1-score": 0.5470459518599562, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.572463768115942, | |
| "recall": 0.6723404255319149, | |
| "f1-score": 0.6183953033268101, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.6243093922651933, | |
| "macro avg": { | |
| "precision": 0.6272912188243995, | |
| "recall": 0.623467168834796, | |
| "f1-score": 0.623202640617811, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.6298032459952319, | |
| "recall": 0.6243093922651933, | |
| "f1-score": 0.6248933520339918, | |
| "support": 724.0 | |
| } | |
| } |