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
File size: 1,154 Bytes
abbab26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"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
} |