Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/cafebert-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 785 Bytes
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},
"refuted": {
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"recall": 0.620253164556962,
"f1-score": 0.6405228758169934,
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},
"not_enough_information": {
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"recall": 0.6638297872340425,
"f1-score": 0.643298969072165,
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},
"accuracy": 0.6906077348066298,
"macro avg": {
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"recall": 0.6886096611790121,
"f1-score": 0.6885226255450633,
"support": 724.0
},
"weighted avg": {
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"recall": 0.6906077348066298,
"f1-score": 0.6905789769345113,
"support": 724.0
}
} |