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": 0.7817460317460317, | |
| "recall": 0.7817460317460317, | |
| "f1-score": 0.7817460317460317, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.6621621621621622, | |
| "recall": 0.620253164556962, | |
| "f1-score": 0.6405228758169934, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.624, | |
| "recall": 0.6638297872340425, | |
| "f1-score": 0.643298969072165, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.6906077348066298, | |
| "macro avg": { | |
| "precision": 0.6893027313027313, | |
| "recall": 0.6886096611790121, | |
| "f1-score": 0.6885226255450633, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.6913983873376138, | |
| "recall": 0.6906077348066298, | |
| "f1-score": 0.6905789769345113, | |
| "support": 724.0 | |
| } | |
| } |