Text Classification
Transformers
Safetensors
Vietnamese
bert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/wikibert-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/wikibert-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/wikibert-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/wikibert-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/wikibert-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_macro_f1_mean": 0.7859431230658753, | |
| "test_macro_f1_std": 0.0078090422016252135, | |
| "test_macro_f1_text": "0.7859 ± 0.0078", | |
| "test_accuracy_mean": 0.7863720073664825, | |
| "test_accuracy_std": 0.008439365920728966, | |
| "test_accuracy_text": "0.7864 ± 0.0084", | |
| "test_macro_precision_mean": 0.7861867400362637, | |
| "test_macro_precision_std": 0.007303026040786383, | |
| "test_macro_precision_text": "0.7862 ± 0.0073", | |
| "test_macro_recall_mean": 0.7869905109603467, | |
| "test_macro_recall_std": 0.008424459089079234, | |
| "test_macro_recall_text": "0.7870 ± 0.0084", | |
| "dev_macro_f1_mean": 0.7795388436295254, | |
| "dev_macro_f1_std": 0.012688247976241145, | |
| "dev_macro_f1_text": "0.7795 ± 0.0127", | |
| "task": "ViFactCheck-gold-evidence", | |
| "dataset": "ViFactCheck", | |
| "model_key": "wikibert", | |
| "model_name": "WikiBERT", | |
| "base_model": "TurkuNLP/wikibert-base-vi-cased", | |
| "seeds": [ | |
| 22, | |
| 42, | |
| 202 | |
| ], | |
| "representative_seed": 202, | |
| "selection_rule": "maximum development Macro-F1; seed ascending tie-break", | |
| "split_policy": "merged_stratified_80_10_10", | |
| "split_seed": 42, | |
| "max_length": 256, | |
| "epochs": 3, | |
| "effective_batch_size": 8 | |
| } |