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
| { | |
| "supported": { | |
| "precision": 0.8117154811715481, | |
| "recall": 0.7698412698412699, | |
| "f1-score": 0.790224032586558, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.654320987654321, | |
| "recall": 0.6708860759493671, | |
| "f1-score": 0.6625, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.628099173553719, | |
| "recall": 0.6468085106382979, | |
| "f1-score": 0.6373165618448637, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.6975138121546961, | |
| "macro avg": { | |
| "precision": 0.6980452141265294, | |
| "recall": 0.6958452854763116, | |
| "f1-score": 0.6966801981438072, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.7005934822022489, | |
| "recall": 0.6975138121546961, | |
| "f1-score": 0.6987822489576734, | |
| "support": 724.0 | |
| } | |
| } |