Text Classification
Transformers
Safetensors
Vietnamese
bert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/wikibert-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/wikibert-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/wikibert-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/wikibert-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/wikibert-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 786 Bytes
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"recall": 0.7896825396825397,
"f1-score": 0.8008048289738431,
"support": 252.0
},
"refuted": {
"precision": 0.7336244541484717,
"recall": 0.7088607594936709,
"f1-score": 0.721030042918455,
"support": 237.0
},
"not_enough_information": {
"precision": 0.836,
"recall": 0.8893617021276595,
"f1-score": 0.8618556701030928,
"support": 235.0
},
"accuracy": 0.7955801104972375,
"macro avg": {
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"recall": 0.7959683337679567,
"f1-score": 0.7945635139984636,
"support": 724.0
},
"weighted avg": {
"precision": 0.7942192125951687,
"recall": 0.7955801104972375,
"f1-score": 0.7945069330763939,
"support": 724.0
}
} |