Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/cafebert-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| seed,dev_macro_f1,test_macro_f1,test_accuracy,test_macro_precision,test_macro_recall,test_weighted_f1,micro_batch_size,gradient_accumulation_steps,effective_batch_size,wall_seconds | |
| 22,0.877927163365246,0.8648359689576693,0.8660220994475138,0.8717446695992885,0.865115061196397,0.8647007066624405,8,1,8,1002.845583677292 | |
| 42,0.8810330368606277,0.8811944805792362,0.8812154696132597,0.8836557872580538,0.880598127271976,0.8810740898651478,8,1,8,598.6296060085297 | |
| 202,0.883914762150928,0.892466134634013,0.8922651933701657,0.8969231895373805,0.8912518863367233,0.8923038164459657,8,1,8,1015.4132843017578 | |