Text Classification
Transformers
Safetensors
Vietnamese
bert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/wikibert-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/wikibert-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/wikibert-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/wikibert-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/wikibert-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,152 Bytes
af42c2d | 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": 0.6228599344021635,
"test_macro_f1_std": 0.0013943969424470462,
"test_macro_f1_text": "0.6229 ± 0.0014",
"test_accuracy_mean": 0.6224677716390423,
"test_accuracy_std": 0.002109841480182199,
"test_accuracy_text": "0.6225 ± 0.0021",
"test_macro_precision_mean": 0.6277920951668107,
"test_macro_precision_std": 0.001803497550642567,
"test_macro_precision_text": "0.6278 ± 0.0018",
"test_macro_recall_mean": 0.6216715330235396,
"test_macro_recall_std": 0.0021704411782186074,
"test_macro_recall_text": "0.6217 ± 0.0022",
"dev_macro_f1_mean": 0.612531129450796,
"dev_macro_f1_std": 0.008510090661649844,
"dev_macro_f1_text": "0.6125 ± 0.0085",
"task": "ViFactCheck-full-context",
"dataset": "ViFactCheck",
"model_key": "wikibert",
"model_name": "WikiBERT",
"base_model": "TurkuNLP/wikibert-base-vi-cased",
"seeds": [
22,
42,
202
],
"representative_seed": 42,
"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
} |