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
| 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 | |
| 22,0.7245918187094658,0.6960405499176284,0.6975138121546961,0.6991543296736428,0.6961205232010511,0.6979188795617406,8,1,8,717.7517776489258 | |
| 42,0.7220630821146287,0.6966801981438072,0.6975138121546961,0.6980452141265294,0.6958452854763116,0.6987822489576734,8,1,8,714.3048448562622 | |
| 202,0.7278703385563059,0.6885226255450633,0.6906077348066298,0.6893027313027313,0.6886096611790121,0.6905789769345113,8,1,8,717.3174591064453 | |